Absolute Improvement Research Articles

EQUITY GI: A prospective study to enhance quality, inclusivity, and trial participation in Black patients with gastrointestinal cancer.

TPS843 Background: Black individuals, including African Americans, experience a disproportionate cancer burden and exhibit the lowest survival rates among all racial groups for gastrointestinal (GI) cancers. The EQUITY GI study seeks to address the significant health disparities encountered by Black patients with GI cancers in the areas of biomarker testing, evidence-based care, clinical trial participation, and health literacy. Our central hypothesis postulates that a comprehensive approach—encompassing appropriate biomarker testing, ensuring evidence-based treatments, support for clinical trial participation, and health literacy initiatives—is essential to effectively mitigate health disparities among Black patients with GI cancers. Methods: The primary objective of this study is to promote equitable care for Black patients with GI cancers by ensuring appropriate biomarker testing and the delivery of evidence-based care. The secondary objectives include promoting clinical trial participation and enhancing health literacy. The specific goals are to increase the rate of evidence-based care, including biomarker testing, to ≥80%, increase the clinical trial enrollment rate from 5% to 15%, and improve the Cancer Health Literacy Test-30 (CHLT-30) score by 30%. Following a comprehensive assessment of the care gap, several initiatives will be implemented: 1. Tracking biomarker testing through a clinical information tracking tool (EQUITY-GI Oncotracker), 2. Providing guidance to treating providers through a molecular tumor board, 3. Facilitating referrals for clinical trials through navigators, and 4. Implementing health literacy interventions measured by CHLT-30 score. We aim to enroll 200 patients over 18 months. With this expected enrollment, the power to detect a ≥60% absolute improvement in the biomarker testing rate (from the current 20% to the expected ≥80%) is over 98%, using a two-sided chi-square test with a significance level of 0.05. To demonstrate an improvement in the clinical trial enrollment rate from 5% to 15%, 18 black patient will be required to get enrolled in a clinical trial through the referrals generated from the current project. To detect a 30% improvement in the mean CHLT-30 score (17 to 22) following health literacy interventions, at least 120 patients will be required, using a two-sided paired t-test with a significance level of 0.05.

Convergent procedure for long-standing persistent atrial fibrillation in heart failure with reduced ejection fraction.

Catheter ablation for atrial fibrillation in patients with heart failure with reduced ejection fraction is associated with a significant reduction in morbimortality. The convergent procedure is a valid ablation option for the treatment of long-standing persistent atrial fibrillation. To describe the outcomes of patients with heart failure with reduced ejection fraction and long-standing persistent atrial fibrillation who underwent the convergent procedure. We studied consecutive patients included in two French centres between 2009 and 2020. Primary endpoint was freedom from any atrial arrhythmia assessed on 24-hour Holter electrocardiogram at 3, 6 and 12 months after the procedure. Left ventricular ejection fraction was assessed on transthoracic echocardiography before and 1 year after the procedure. All patients had at least 12 months of follow-up. Forty-three patients were included (86% were men). Baseline left ventricular ejection fraction was 38±10.5% and indexed left atrial volume was 50±27mL/m2. Among the study population, 34 patients (79%) were free from atrial fibrillation/tachycardia at the end of follow-up. No periprocedural death occurred. We observed two groin haematomas and four mild pericardial effusions. At 12-month follow-up, 21 patients (49%) were still on antiarrhythmic drug therapy, and a reduction in antiarrhythmic drug dosage was achieved in 10 patients (23%). The absolute median improvement in left ventricular ejection fraction was 8% at 12 months (P=0.003). The convergent procedure has been shown to be effective and safe for patients with patients with heart failure with reduced ejection fraction and long-standing persistent atrial fibrillation, with significant left ventricular function improvement.

Left ventricular function improvement during angiotensin receptor-neprilysin inhibitor treatment in a cohort of HFrEF/HFmrEF patients.

Heart failure (HF) patients may lack improvement of left ventricular (LV) ejection fraction (LVEF) despite optimal HF medication comprising an angiotensin receptor-neprilysin inhibitor (ARNI). Therefore, we aimed to identify key predictors for LV functional enhancement and prognostic reverse cardiac remodelling in HF patients on ARNI treatment. We retrospectively analysed 294 consecutive patients with HF with reduced (HFrEF) or mildly reduced (HFmrEF) ejection fraction in our 'EnTruth' patient registry. LVEF was determined by echocardiography at initiation of ARNI and at 12months of follow-up. We assessed the predictive value of clinically relevant patient-, HF- and treatment-related parameters in regard to changes in LVEF and all-cause mortality using medoid clustering and the XGBoost machine learning algorithm. Cluster analysis integrating clinically relevant patient characteristics unveiled four characteristic sub-phenotypes of patients with HFrEF and HFmrEF, respectively. Distinct clusters exhibit a strong (P<0.05) therapeutic response to ARNI treatment and enhanced LV function. Key patient criteria, such as duration and aetiology of HF, renal function and de novo ARNI treatment, were significantly (P<0.05) associated with change of LVEF and independently predicted cardiac remodelling. By training various machine learning models on relevant clinical parameters, stratification of LVEF improvement by XGBoost resulted in a high prediction accuracy. The stratification of patients with HFrEF [area under the receiver operating characteristic curve (AUC)=0.77] and HFmrEF (AUC=0.70) led to an increased diagnostic accuracy of LVEF improvement in the validation cohort. Using machine learning, the likelihood of cardiac remodelling following ARNI treatment, as indicated by our newly established EnTruth score, was directly associated with absolute LVEF improvement in both HFrEF (r=0.51, P<0.0001) and HFmrEF (r=0.42, P=0.001). Ultimately, patients with HFrEF and a high EnTruth score have a lower risk of all-cause mortality (P<0.05 in survival analysis). Recognition of essential clinical factors by integrating machine learning and cluster analyses may help to identify HF patients benefiting from improvement of LVEF following ARNI treatment. Early identification of those patients with a high response to ARNI treatment may allow a more refined selection of patients benefiting from an early escalation of HF treatment or interventional therapy.

Drift Correction Method of Wastewater Treatment Model Based on Transfer Learning Across Time Scales

Data is the core foundation of intelligent operation and maintenance, but currently, there is generally insufficient data for wastewater treatment plants, and the status of wastewater treatment systems dynamically evolves with the changes in the internal and external environment. The intelligent operation and maintenance of wastewater plants face difficulties in modeling and model drift caused by system evolution. In response to this issue, the summer and winter seasons with significant differences in wastewater temperature, wastewater quality, and microbial status were selected as typical comparison scenarios. The mechanism model was combined with neural networks to establish a wastewater treatment model drift correction method based on cross-time scale transfer learning. Firstly, in response to the problem of insufficient data, an activated sludge model （ASM） was established and calibrated. Summer operating data was used as input to simulate and calculate operating parameters and effluent data, generating a simulated operating dataset to achieve data augmentation and quality improvement. This was used to train a multi-layer perceptron neural network （MLP） model. The results showed that the average simulation accuracy of the MLP model for summer effluent COD, ammonia nitrogen, total phosphorus, etc., was all over 95%. This indicates the feasibility of training MLP models based on ASM-generated data. Then, the MLP model was used to guide the operation of the pilot A2O project. Experimental data analysis showed that the model drift phenomenon was significant in the field of wastewater treatment. During the operation of the pilot plant guided by the summer model, the accuracy of the predicted values gradually decreased, and the average prediction accuracy of the model for effluent COD gradually decreased from 98.14% to 75.18%. The phenomenon of model drift required effective correction to maximize the effectiveness of the model. In response to the problem of model drift caused by a significant decrease in simulation accuracy in winter operating conditions, a transfer learning approach was introduced. The winter measured data was used as the target domain dataset, and the MLP model was used as the pre-trained model for transfer learning. The experimental results showed that transfer learning methods can significantly improve model performance. After transfer learning, the average simulation accuracy of the MLP model for effluent COD, ammonia nitrogen, total nitrogen, and total phosphorus was relatively improved by 28.58%, 184.44%, 207.56%, and 100.51%, with absolute improvement values of 21.49%, 60.79%, 58.14%, and 46.74%, respectively. This indicates that the cross-time scale transfer learning method proposed in this study can significantly improve model performance, effectively solve model drift problems, and achieve a model-following response to the dynamic evolution of wastewater treatment systems. This study indicates that transfer learning based on pre-trained models only requires a small amount of engineering data and computational complexity to achieve model updating and correction. Compared to model retraining, this method reduces computational complexity and reduces the dependence on engineering data during data-driven model updates.

Does the Relationship Between Preoperative Function and Achievement of Clinically Important Benchmarks of Success After Total Shoulder Arthroplasty Depend on Outcome Assessment Design?

A variety of clinically important benchmarks of success (CIBS) have been defined for total shoulder arthroplasty (TSA) to quantify success. However, it is unclear how the preoperative status of the patient influences their likelihood of achieving each CIBS. (1) What proportion of patients achieve commonly used CIBS after TSA? (2) Is there a relationship between a patients' preoperative function and their probability of achieving different CIBS? (3) Does there exist preoperative ranges for each outcome measure that are associated with greater achievement of CIBS? We retrospectively queried a multicenter shoulder arthroplasty database for primary anatomic TSA (aTSA) and reverse TSA (rTSA). A total of 2041 aTSAs and 3205 rTSAs were included. The mean ± SD age at time of surgery was 66 ± 9 years for aTSAs and 72 ± 8 years for rTSAs. In all, 51% (1049 of 2041) of aTSAs and 61% (1955 of 3205) of rTSAs were in patients who were women. For aTSAs, osteoarthritis with an intact rotator cuff was the most common preoperative diagnosis (92% [1869 of 2041]). For rTSAs, the most common preoperative diagnoses included rotator cuff tear arthropathy (40% [1280 of 3205]), osteoarthritis with an intact rotator cuff (24% [780 of 3205]), osteoarthritis with a torn rotator cuff (20% [632 of 3205]), and massive rotator cuff tear (10% [309 of 3205]). Outcomes were evaluated at latest follow-up (aTSA 61 ± 36 months, rTSA 47 ± 26 months) and consisted of ROM (abduction, forward elevation, external and internal rotation) as well as the most commonly used outcome scores: the Simple Shoulder Test (SST), Constant score, American Shoulder and Elbow Surgeons (ASES) score, University of California, Los Angeles (UCLA) score, Shoulder Pain and Disability Index (SPADI), and the Shoulder Arthroplasty Smart (SAS) score. The CIBS that we evaluated included the minimum clinically important difference (MCID), substantial clinical benefit (SCB), patient acceptable symptom state (PASS), minimum clinically important percentage of maximal possible improvement (MCI-%MPI), and substantial clinically important percentage of maximal possible improvement (SCI-%MPI). Prosthesis-specific anchor-based CIBS were adopted from prior publications on patients from this database. Multivariable regression was performed to identify the relationship between preoperative outcome measures and achievement of CIBS. Additionally, receiver operating characteristic (ROC) curve analyses were performed to determine whether thresholds in preoperative outcome measures were associated with achieving CIBS. For all ROM measures and outcome scores, poorer preoperative ROM was associated with greater odds of achieving the MCID and SCB but lower odds of achieving the PASS. For the SST and the two scores without ceiling effects (the Constant and SAS scores), poorer preoperative outcome scores were associated with greater odds of achieving the MCI-%MPI and SCI-%MPI, but no association was demonstrated for the ASES, SPADI, and UCLA scores. Graphical analysis demonstrated that patients with greater preoperative ROM and outcome scores had a lower probability of achieving the MCID and SCB but a higher probability of achieving the PASS. For outcome scores with known ceiling effects, patients with more favorable preoperative outcome scores were more likely to achieve the MCI-%MPI and SCI-%MPI than the MCID and SCB, respectively. For outcome scores without ceiling effects, patients undergoing aTSA were more likely to achieve the MCID, MCI-%MPI, and the SCI-%MPI than the SCB, but no clear trend was identified for patients undergoing rTSA. On ROC curve analysis, identified thresholds were reasonably accurate (area under the curve > 0.7) for achievement of measures of absolute improvement (the MCID and SCB), but not for absolute postoperative status (the PASS) or relative improvement (the MCI-%MPI and SCI-%MPI). While most patients reported being "much better" (aTSA 75%, rTSA 76%) or "better" (aTSA 15%, rTSA 18%) compared with before surgery, 10% of aTSAs and 6% of rTSAs were either "unchanged" or "worse." Patients' likelihood of achieving CIBS depends in part on their baseline function and whether success is defined as absolute improvement, absolute postoperative status, or relative improvement. Patients with more favorable preoperative status are more likely to achieve CIBS that evaluate relative improvement (the MCI-%MPI and SCI-%MPI) than absolute improvement (the MCID and SCB). Future studies are needed to determine which individual CIBS or combinations thereof most accurately represent clinically relevant benefit. Level III, therapeutic study.

Leveraging Attention Mechanisms in Neural Networks for High‐Order Accuracy in Particle‐in‐Cell Simulations

This paper introduces a novel approach to enhance the accuracy of charge density calculations in particle‐in‐cell (PIC) simulations by leveraging attention mechanisms in neural networks (NNs). We propose a three‐network architecture comprising a 1st‐order‐pre‐trainer, a 2nd‐order‐improver, and a discriminator network. The 1st‐order‐pre‐trainer is initially trained on a large dataset to predict charge densities from particle positions and charges. Subsequently, it is fine‐tuned with the 2nd‐order‐improver and discriminator network using a smaller dataset to achieve near 2nd‐order accuracy. Our approach substitutes the traditional particle interpolation process in JefiPIC with a meticulously trained NN. The results demonstrate that PIC simulations enhanced by NNs can simulate plasmas and electromagnetic (EM) fields with significantly enhanced accuracy, achieving an absolute accuracy improvement exceeding 10% when compared to a 1st‐order PIC simulator and a relative accuracy improvement of over 50% in comparison to other direct training techniques. Furthermore, our method has the potential to diminish the reliance on acquiring real labels, which are often difficult to observe. This work highlights the potential of integrating artificial intelligence (AI) techniques to improve the precision and efficiency of PIC simulations, paving the way for future advancements in plasma and nuclear physics research.

Integration of a specialist pharmacist-led multidisciplinary team in primary care: preventing strokes in people with atrial fibrillation across North East London

Public Health England outlines a national ambition of anticoagulating 90% of eligible patients with atrial fibrillation (AF) by 2029. In 2019/2020, two out of three boroughs reviewed in this study...

Transforaminal Lumbar Endoscopic Discectomy: A Novel Alternative for Management of Lumbar Disc Herniation in Patients With Rheumatoid Arthritis?

Lumbar disc herniation (LDH) represents an increasingly encountered condition in patients with rheumatoid arthritis (RA). The aim of the present study is to assess the progress of health-related quality of life following transforaminal endoscopic lumbar discectomy (TELD) for LDH in patients suffering from RA. Seventy-four patients, scheduled to undergo elective TELD for LDH, were prospectively enrolled in the study. Group A included 36 otherwise healthy individuals and group B 38 patients complementarily diagnosed with RA according to the 2010 ACR/EULAR (American College of Rheumatology/European League Against Rheumatism) criteria. The Medical Outcomes Study 36-item Short Form health survey (SF-36) was selected for the outcome assessment at baseline and postoperatively, at selected intervals at 6 weeks, 3, 6, and 12 months postoperatively. Group A presented statistically significantly higher scores in all SF-36 domains and all selected intervals (p<0.001), except for mental health parameter. All aspects of SF-36 questionnaire significantly improved postoperatively (p<0.001) and in each group independently. Nevertheless, the absolute improvement between consecutive time intervals did not differ significantly between the 2 groups. Patients diagnosed with RA who undergo TELD for LDH demonstrate statistically significant improvement in their health status, as measured by SF-36 questionnaire, one year after the procedure. This improvement is comparable with normal individuals.

Association of intravitreal and topical anti-inflammatory therapies on short-term anatomical and functional outcomes following epiretinal membrane surgery.

Here we examine the association of anti-inflammatory therapy with anatomical and functional outcomes of epiretinal membrane surgery. The study included consecutive patients having gone through epiretinal membrane surgery at Helsinki University Hospital, Finland, between 2017 and 2021. The association of perioperative intravitreal and postoperative topical anti-inflammatory therapies with surgical outcomes was assessed. In total, 214 eyes of 214 patients with a mean age of 71.2 ± 8.2 years were studied. At 1-month, perioperative intravitreal use of triamcinolone acetonide (n = 27) was associated with a significant proportional and absolute improvement in foveal thickness (-15.9 ± 18.4% vs. -4.2 ± 25.6%, p = 0.003 and -86.2 ± 109.6 μm vs. -33.7 ± 94.3 μm, p = 0.004), and central subfield macular thickness (-14.7 ± 16.5% vs. -6.3 ± 16.9%, p = 0.009 and -80.6 ± 102.8 μm vs. -36.1 ± 75.9 μm, p = 0.004) when compared to those without triamcinolone acetonide. Furthermore, best-corrected visual acuity (BCVA) gain showed a non-significant yet corresponding trend favouring intraoperative intravitreal use of triamcinolone acetonide (0.21 ± 0.27 vs. 0.09 ± 0.28 LogMAR units, p = 0.062). Postoperative use of topical non-steroidal anti-inflammatory drugs (NSAIDs) (n = 36) as adjunct therapy showed no significant advantage in anatomical outcomes or BCVA gain when compared to those without NSAIDs (all p > 0.05). Triamcinolone acetonide remained significant for proportional and absolute improvement in foveal thickness (p = 0.019 and p = 0.006) and in central subfield macular thickness (p = 0.013 and p = 0.006) when controlled for confounding factors patient age and gender and topical NSAID use. Intraoperative intravitreal use of triamcinolone acetonide improved short-term anatomical outcomes in patients who underwent epiretinal membrane surgery.

Attention-Based Malware Detection Model by Visualizing Latent Features Through Dynamic Residual Kernel Network.

In recent years, significant research has been directed towards the taxonomy of malware variants. Nevertheless, certain challenges persist, including the inadequate accuracy of sample classification within similar malware families, elevated false-negative rates, and significant processing time and resource consumption. Malware developers have effectively evaded signature-based detection methods. The predominant static analysis methodologies employ algorithms to convert the files. The analytic process is contingent upon the tool's functionality; if the tool malfunctions, the entire process is obstructed. Most dynamic analysis methods necessitate the execution of a binary file within a sandboxed environment to examine its behavior. When executed within a virtual environment, the detrimental actions of the file might be easily concealed. This research examined a novel method for depicting malware as images. Subsequently, we trained a classifier to categorize new malware files into their respective classifications utilizing established neural network methodologies for detecting malware images. Through the process of transforming the file into an image representation, we have made our analytical procedure independent of any software, and it has also become more effective. To counter such adversaries, we employ a recognized technique called involution to extract location-specific and channel-agnostic features of malware data, utilizing a deep residual block. The proposed approach achieved remarkable accuracy of 99.5%, representing an absolute improvement of 95.65% over the equal probability benchmark.

Differences in uni-dimensional breathlessness measures and thresholds for clinical response in a randomised controlled trial in people with chronic breathlessness: an exploratory study

IntroductionOptimally measuring improvements in chronic breathlessness in clinical practice and research continues to evolve. The aim of this study was to consider the performance of uni-dimensional measures in chronic breathlessness...

The Association Between Delirium Upon Admission to a Rehabilitation Hospital and Motor Rehabilitation Outcomes Among Hip Fracture Surgery Patients: A Historical Cohort Study.

Background: Delirium is a common neuropsychiatric syndrome characterized by the acute and fluctuating impairment of cognition, attention, and consciousness, which is prevalent in older adults following surgical procedures. Despite the recognized impact of delirium on recovery, its specific effects on motor rehabilitation outcomes in the geriatric population remain underexplored. This historical cohort study aimed to evaluate the association between the presence of delirium upon admission to a rehabilitation hospital and the motor functional gain at discharge among older patients following hip fracture surgery. Methods: The collected data included socio-demographic characteristics, comorbidities, medications, Mini-Mental State Examination (MMSE) scores, and the Functional Independence Measure (FIM). Motor rehabilitation outcomes were assessed using Motor Absolute Functional Gain (mAFG), the Montebello Rehabilitation Factor Score (mMRFS), and Rehabilitation Efficiency (mRE). Results: Of the 143 hip fracture patients admitted for rehabilitation, 38 (26.6%) were diagnosed with delirium. Patients with delirium had lower MMSE scores (18.1 ± 5.8 vs. 22.4 ± 6.0, p < 0.001), higher benzodiazepine prescription rates (50.0% vs. 14.3%, p < 0.001), and longer lengths of stay in acute care and rehabilitation (42.7 ± 10.4 vs. 37.3 ± 11.2 days, p = 0.01). Despite significant improvements in the FIM scores for both groups (p < 0.001), patients with delirium had lower mAFG (11.87 ± 7.26 vs. 15.91 ± 8.73, p = 0.01), mMRFS (0.22 ± 0.14 vs. 0.31 ± 0.15, p = 0.001), and mRE (0.28 ± 0.17 vs. 0.44 ± 0.25, p < 0.001). However, the multivariate regression models showed no association between delirium and functional improvement after adjusting for confounders. Conclusions: While both patients with and without delirium showed improvement in their motor functions by the time they were discharged from a rehabilitation hospital, patients with delirium showed lower absolute and relative improvements. Tailored programs addressing the special needs of patients with delirium after hip fracture surgery may enhance outcomes for this vulnerable population. A specialized, multidisciplinary approach tailored to the patient's cognitive status and overall condition is key to maximizing the recovery of older hip fracture patients with delirium.

Concept-aware embedding for logical query reasoning over knowledge graphs

Logical query reasoning over knowledge graphs (KGs) is an important task for querying some information upon specified conditions. Despite recent advancements, existing methods typically focus on the inherent structure of logical queries and fail to capture the commonality among entities and relations, resulting in cascading errors during multi-hop inference. To mitigate this issue, we resort to inferring relations’ domain constraints based on the commonality of their connected entities implicitly. Specifically, to capture the domain constraints of relations, we treat the set of relations emitted by an entity as its implicit concept information and derive a relation’s domain constraint by aggregating the implicit concept information of its head entities. Employing a geometric-based embedding strategy, we enrich the representations of entities in the query with their implicit concept information. Additionally, we design a straightforward yet effective curriculum learning strategy to refine its reasoning skills. Notably, our model can be integrated into any existing query embedding-based logical query reasoning methods in a plug-and-play manner, enhancing their understanding of the entities as well as relations in queries. Experiments on three widely used datasets show that our model can achieve comparable outcomes and improve the performance of existing logical query reasoning models. Particularly, as a plug-in, it achieves an absolute improvement of the maximum 8.4% Hits@3 compared to the original model on the FB15k dataset, and it surpasses the former state-of-the-art plug-and-play logical query reasoning model in most scenes, exceeding it by up to 2.1% average Hits@3 results.

Cross-Modal self-supervised vision language pre-training with multiple objectives for medical visual question answering

Medical Visual Question Answering (VQA) is a task that aims to provide answers to questions about medical images, which utilizes both visual and textual information in the reasoning process. The absence of large-scale annotated medical VQA datasets presents a formidable obstacle to training a medical VQA model from scratch in an end-to-end manner. Existing works have been using image captioning dataset in the pre-training stage and fine-tuning to downstream VQA tasks. Following the same paradigm, we use a collection of public medical image captioning datasets to pre-train multimodality models in a self-supervised setup, and fine-tune to downstream medical VQA tasks. In the work, we propose a method that featured with Cross-Modal pre-training with Multiple Objectives (CMMO), which includes masked image modeling, masked language modeling, image-text matching, and image-text contrastive learning. The proposed method is designed to associate the visual features of medical images with corresponding medical concepts in captions, for learning aligned vision and language feature representations, and multi-modal interactions. The experimental results reveal that our proposed CMMO method outperforms state-of-the-art methods on three public medical VQA datasets, showing absolute improvements of 2.6%, 0.9%, and 4.0% on the VQA-RAD, PathVQA, and SLAKE dataset, respectively. We also conduct comprehensive ablation studies to validate our method, and visualize the attention maps which show a strong interpretability. The code and pre-trained weights will be released at https://github.com/pengfeiliHEU/CMMO.

Effects of Mulligan technique in knee tendinopathy in basketball players

Introduction:Tendinopathy considered as a problem present in sports, like in basketball players.Patellar tendon injuries in the sport of basketball are widespread bigger compared to other sported nearly (21,5%)of injuries.The purpose of aour study is to evaluate the effect of the Mulliagn technique on tendinopathy of the knee in basketball payers. Objectives: The objectives of this study is to evaluate the effect of the mulligan technique in the case of tendinopathy. How does the mulligan technique affect the improvement of the articular amplitudes for the knee joint and how does this technique affect the improvement of the pain level. Methods: This a case report. professional basketball in Gega basketball team 19-year-old male. During the routine assessment, the basketball player complains of pain while palpating the patellar tendon. Several tests were applied like the one-leg stand test SLDS and the Royal London Hospital test. Results: This study evaluates positive values for the pain scale; NPRS test from grade 6 pain pre -treatment in 2 and 0 scale of pain after first session e third session of mulligan technique. Also, regarding the articular amplitude, especially the flexion, we noticed its improvement from the first day after the treatment to reaching the final flexion amplitudes in the second treatment. Conclusions: The Mulligan technique in this case report of the basketball player of the Gega basketball team were effective in two important variables, range of motion of knee joint, especially for the flexion movement, and the absolute improvement of pain.

Learning and consolidating the contextualized contour representations of tones from F0 sequences and durational variations via transformers

Many speech characteristics, including conventional acoustic features such as mel frequency cepstrum coefficients and mel-spectrograms, as well as pre-trained contextualized acoustic representations such as wav2vec2.0, are used in a deep neural network or successfully fine-tuned with a connectionist temporal classification for Mandarin tone classification. In this study, the authors propose a transformer-based tone classification architecture, TNet-Full, which uses estimated fundamental frequency (F0) values and aligned boundary information on syllables and words. Key components of the model framework are the contour encoder and rhythm encoder, as well as the cross-attention between contours and rhythms established in the interaction encoder. Using contextual tonal contours as a reference, as well as rhythmic information derived from duration variations to consolidate more on contour representations for tone recognition, TNet-Full achieves absolute performance improvements of 24.4% for read speech (from 71.4% to 95.8%) and 6.3% for conversational speech (from 52.1% to 58.4%) when compared to a naive, simple baseline transformer, TNet-base. The relative improvements are 34.2% and 12.1%. As humans perceive tones, contour abstractions of tones can only be derived from F0 sequences, and tone recognition would be improved if syllable temporal organization was stable and predictable instead of fluctuating as seen in conversations.

Adjuvant Pertuzumab and Trastuzumab in Early Human Epidermal Growth Factor Receptor 2-Positive Breast Cancer in the APHINITY Trial: Third Interim Overall Survival Analysis With Efficacy Update.

Clinical trials frequently include multiple end points that mature at different times. The initial report, typically based on the primary end point, may be published when key planned co-primary or secondary analyses are not yet available. Clinical Trial Updates provide an opportunity to disseminate additional results from studies, published in JCO or elsewhere, for which the primary end point has already been reported.The APHINITY trial (ClinicalTrials.gov identifier: NCT01358877) previously demonstrated that pertuzumab added to adjuvant trastuzumab and chemotherapy improved invasive disease-free survival (iDFS) for patients with early human epidermal growth factor receptor 2-positive (HER2+) breast cancer (BC). Here, we report the preplanned third interim analysis of overall survival (OS) and a descriptive updated iDFS analysis with 8.4 years of median follow-up of 4,804 patients in the intent-to-treat population. The 8-year OS was 92.7% in the pertuzumab versus 92.0% in the placebo group (hazard ratio [HR], 0.83 [95% CI, 0.68 to 1.02]; P = .078, above the 0.006 significance threshold). The HR was 0.80 [95% CI 0.63 to 1.00] in the node-positive cohort and 0.99 [95% CI, 0.64 to 1.55] in the node-negative cohort. Updated results of 8-year iDFS in the node-positive cohort showed an absolute improvement of 4.9% favoring pertuzumab (86.1% v 81.2%; HR, 0.72 [95% CI, 0.60 to 0.87]). The node-negative cohort did well without adding pertuzumab (8-year iDFS and OS in the placebo group were 93.3% and 96.4%, respectively). The iDFS benefit was seen in the hormone receptor-negative (HR, 0.82 [95% CI, 0.64 to 1.06]) and HR+ cohorts (HR of 0.75 [95% CI, 0.61 to 0.92]). Despite improvement in overall iDFS, the addition of pertuzumab did not improve OS at this third interim analysis.

The Improvement of Intraoperative Motor Evoked Potential after Decompression in Cervical Compressive Myelopathy: Its Significance and Related Factors

Objective: This study investigated the relationship between intraoperative motor evoked potential (MEP) improvement after decompression surgery for cervical compressive myelopathy (CCM) and postoperative neurological outcomes, and preoperative factors influencing MEP improvement.Methods: MEP amplitudes were measured prospectively before and after decompression in 38 patients with CCM. The patients were categorized into three groups according to whether the intraoperative MEP slightly decreased, slightly increased, or significantly increased. Functional outcomes were assessed using the recovery rate (RR) and absolute improvement (AI) of the modified Japanese Orthopaedic Association score on postoperative days (PODs) 7 and 28. The preoperative characteristics and intraoperative MEP changes among the three groups were compared. Additionally, the correlation between the increase in MEP amplitude during surgery and the extent of improvement in functional outcomes was investigated.Results: The significantly increased MEP group had a lower baseline MEP amplitude (152.46 µV; p=0.009). In the slightly decreased MEP group, the RR was 27.98 ± 32.29% at POD 7 (p=0.010) and 11.61 ± 69.84% at POD 28 (p=0.200); the AI was 0.79 ± 0.80 at POD 7 (p=0.010) and 0.79 ± 1.42 at POD 28 (p=0.100). In the slightly increased MEP group, the RR was 23.75 ± 28.36% at POD 7 (p=0.040) and 28.47 ± 43.38% at POD 28 (p=0.070); the AI was 1.00 ± 1.21 at POD 7 (p=0.030) and 1.08±1.88 at POD 28 (p=0.100). In the significantly increased MEP group, the RR was 41.06 ± 32.01% at POD 7 (p=0.009) and 59.78 ± 34.52% at POD 28 (p=0.006); the AI was 3.08 ± 2.07 at POD 7 (p=0.009) and 4.33 ± 2.54 at POD 28 (p=0.006). Greater intraoperative MEP improvement correlated with better postoperative recovery at 1 month (RR, p=0.010; AI, p&lt;0.001).Conclusion: Intraoperative MEP monitoring is valuable for predicting postoperative neurological outcomes in CCM patients, particularly those with lower baseline MEP amplitudes. Significant intraoperative MEP improvements are associated with better functional recovery. These findings underscore the importance of MEP monitoring in optimizing surgical strategies and predicting neurological recovery.

Epidemiology, clinical characterization, and outcomes for heart failure with improved ejection fraction

Abstract Background Contemporary guidelines propose a new classification of heart failure with improved ejection fraction (HFimpEF). The prevalence of HFimpEF is expected to increase with the implementation of guideline-directed medical therapy (GDMT); however, there are limited data on the epidemiology of and outcomes for this novel clinical entity. Purpose We describe the prevalence, GDMT use and outcomes among patients with HFimpEF within a large, integrated healthcare delivery system. Methods We identified adults ≥18 years with incident HF with reduced EF (HFrEF ≤40%) between January 2013 and December 2018 from a large, integrated healthcare system serving &amp;gt;4.5 million members. HFimpEF was defined as known HFrEF with a follow-up EF measurement &amp;gt;40% within 12 months after incident HFrEF. Among HFimpEF patients, we examined GDMT use at discharge after incident HFrEF, incident HFimpEF, and at 6- and 12-months post-HFimpEF. From 12 months post initial HFrEF diagnosis, we calculated incidence rates of subsequent worsening HF (WHF) events (i.e., HF-related hospitalization, emergency department or urgent outpatient visits adjudicated using validated natural language processing-based algorithms) and all-cause death through December 31, 2019. We compared rates between patients with HFimpEF and those with persistent HFrEF (i.e., having all LVEF measurements within 12 months of incident HFrEF ≤40%) using Cox proportional hazards models adjusted for the EF value at incident HFrEF. Results Among 12,639 patients with incident HFrEF, 4,124 (33%) experienced HFimpEF within 12 months. Among patients with HFimpEF, use of β-blockers (82%) and angiotensin-converting enzyme inhibitor/angiotensin II receptor blockers (86%) was high, while angiotensin receptor-neprilysin inhibitor (1.2%) or mineralocorticoid receptor antagonist (28.2%) use was low. β-blockers, ACEi/ARB, and MRA use declined after incident HFimpEF (Figure, Panel A). Rates of WHF were 19.7 (95% Confidence Interval [CI]: 18.7 to 20.6) per 100 person-years among patients with HFimpEF vs. 40.0 (95% CI: 38.5 to 41.5) among patients with persistent HFrEF (hazard ratio [HR]: 0.55, 95% CI: 0.49 to 0.62; Figure, Panel B). Rates of death were 5.2 (95% CI: 4.7 to 5.7) and 8.4 (95% CI: 7.8 to 9.1) per 100 person-years among HFimpEF and persistent HFrEF, respectively (HR: 0.59, 95% CI: 0.52 to 0.67). Among patients with HFimpEF, those with an absolute EF improvement of ≥20% were less likely to experience WHF (HR: 0.82, 95% CI: 0.67 to 0.99) and death (HR: 0.85, 95% CI: 0.69 to 1.04) compared with those with &amp;lt;20% improvement. Conclusions One in three patients experienced improvement in EF within 12 months of initial HFrEF diagnosis. HFimpEF is associated with a reduced risk of WHF events and death, although these patients remain at significant clinical risk. Further research is necessary to assess the potential impact of sustained adherence to GDMT and therapeutic optimization on HFimpEF patient outcomes.

Albumin creatinine index added to the grace score increases its discriminative capacity for mortality at 12 years of follow-up in patients with acute coronary syndrome

Abstract introduction There is currently no information on the prognostic value of the GRACE score with the additional addition of the albumin-creatinine index (ABI) for the prediction of very long-term mortality in unselected populations of acute coronary syndromes (ACS). Objectives To evaluate the additive value of the albumin-creatinine index in spontaneous urine sample to the GRACE score for the prediction of 12-year mortality from all causes in an unselected cohort of patients with acute coronary syndrome. Material and methods A prospective cohort study was carried out, which included patients admitted to a Community Hospital with a diagnosis of ACS. These patients had their IAC measured in a single spontaneous urine sample based on the principle of immunoturbidimetry in the first 24 hours of admission. The end point of the analysis was all-cause mortality at follow-up. The GRACE 2.0 score was calculated for all patients in the study. The scores were compared according to the following metrics: their power to discriminate between events and non-events (ROC Areas), calibration or goodness of fit, evaluating the agreement between the proportion of events and non-events expected by both scores observed by Hosmer Lemeshow Test. The comparison between the areas under the ROC Curves was carried out using De Long's test with the MEDCALC program version 19.1.7. The net reclassification index and its 95% CI (GRACE score + IAC/GRACE score) were evaluated. Results 600 patients with ACS were analyzed, with an average age of 64 years. The GRACE score risk subgroups were divided by the established cut-off values: low risk (0 to 108), intermediate (109 to 139) and high risk (greater than 139). The area under the curve of the Grace score for mortality during follow-up was 0.79 (95% CI 0.69-0.89), while the area under the ROC curve of the GRACE + IAC score was 0.81 (95% CI 0.74-0.87). ). Comparing the two areas by the De Long test, the difference was statistically significant (P &amp;lt; 0.0001; 95% CI 0.07- 0.015). On the other hand, contingency tables were developed and the Hosmer Lemeshow Test (goodness of fit) was performed on the GRACE score and the GRACE + IAC score, observing agreement between expected events and non-events, throughout all subgroups of risk. In addition, reclassifications were made into deceased and survivors by applying the GRACE + IAC score on the GRACE score. The net reclassification rate was 28% (95% CI 21-35). The chi square of its Mc Nemar Test was 3.69 p &amp;lt; 0.0002. The absolute comprehensive discriminatory improvement was 0.01 (95% CI 0.009-0.02) and the corresponding relative value was 8.2% (95% CI 7.9-8.5). Conclusions In this unselected ACS cohort, the albumin-creatinine index added to the GRACE score significantly increases its ability to discriminate mortality from all causes in a 12-year follow-up.Comparison between ROC curvesHosmer Lemeshow test