Underreporting of Pain for Short-Stay Nursing Home Residents in the Minimum Data Set 3.0?: Staff-Report, Self-Report, and the Role of Cognitive Impairment and Racial/Ethnic Identity.

Advancing CAR-T Cell Manufacturing in Latin America: Current Landscape, Future Directions, and Challenges.

Deep learning (DL) models are widely adopted in biomedical imaging, where image segmentation is increasingly recognized as a quantitative tool for extracting clinically meaningful information. However, model performance critically depends on dataset size and training configuration, including model capacity. Traditional sample size estimation methods are inadequate for DL due to its reliance on high-dimensional data and its nonlinear learning behavior. To address this gap, we propose a DL-specific framework to estimate the minimal dataset size required for stable segmentation performance. We validate this framework across two distinct clinical tasks: colorectal polyp segmentation from 2D endoscopic images (Kvasir-SEG) and glioma segmentation from 3D brain MRIs (BraTS 2020). We trained residual U-Nets—a simple, yet foundational architecture—across 200 configurations for Kvasir-SEG and 40 configurations for BraTS 2020, varying data subsets (2%–100% for the 2D task and 5%–100% for the 3D task). In both tasks, performance metrics such as the Dice Similarity Coefficient (DSC) consistently improved with increasing data and depth, but gains invariably plateaued beyond approximately 80% data usage. The best configuration for polyp segmentation (6 layers, 100% data) achieved a DSC of 0.86, while the best for brain tumor segmentation reached a DSC of 0.79. Critically, we introduce a surrogate modeling pipeline using Long Short-Term Memory (LSTM) networks to predict these performance curves. A simple uni-directional LSTM model accurately forecasted the final DSC, accurately forecasting the final DSC with low mean absolute error across both tasks. These findings demonstrate that segmentation performance can be reliably estimated with lightweight models, suggesting that collecting a moderate amount of high-quality data is often sufficient for developing clinically viable DL models. Our framework provides a practical, empirical method for optimizing resource allocation in medical AI development.

BackgroundThe Minimum Data Set (MDS) is a tool developed by the Centers for Medicare & Medicaid Services (CMS) to standardize assessment for facilitating care management in long‐term care facilities. The integration of biosensor data into the assessment could provide clinicians with real‐time and longitudinal information that allow for an unprecedented insight into the physical and cognitive health of the individual.MethodAn example of an area where biosensor data could be integrated into the MDS in Section G0300. Balance During Transitions and Walking. Currently, the assessment is based on eye‐witness of the clinician and uses the following coding scores. 0. Steady at all times.1. Not steady, but able to stabilize without staff assistance. 2. Not steady, only able to stabilize with staff assistance. 8. Activity did not occur. For the following activities A. Moving from seated to standing position. B. Walking (with assistive device if used). C. Turning around and facing the opposite direction while walking. D. Moving on and off toilet. E. Surface‐to‐surface.An individual using a biosensor for gait analysis could both reduce the amount of time for assessment and provide more accurate and objective information. Continuous monitoring is feasible with this approach along with more sophisticated detection of movement and gait anomalies. Several solutions may be appropriate for further research including inertial measurement units, pressure‐sensitive insoles, radar‐based monitoring, and depth‐sensing cameras.ResultThe biosensing method combined with MDS codes could utilize the following data driven criteria. 0. No significant deviation in step symmetry, sway velocity, or transition stability. 1. Minor deviations detected but corrected within a threshold time (e.g., <1.5s delay in stability) 2. High sway, excessive corrective steps, prolonged stabilization time (>2s). 8. No sensor data recorded for the movement.ConclusionIntegrating gait analysis sensors into MDS Section G0300 would enhance assessment accuracy, reduce subjectivity, and provide real‐time fall risk insights. This could be particularly beneficial in skilled nursing facilities, rehabilitation centers, and fall prevention programs.

BackgroundOlder adults with dementia face substantial challenges to their mental status due to declining physical function and deteriorating lifestyle, which negatively impacts their quality of life. Mental status is a key determinant of daily functioning in progressive dementia, but assessments often require substantial resources and may be inaccessible to many. Leveraging Electronic Health Records (EHRs) and large language models (LLMs) to predict mental status offers a promising alternative for improving patient management, risk assessment, and timely interventions.MethodThis retrospective cohort study analyzed EHRs of patients aged ≥65 years who were diagnosed with dementia between 2013 and 2020 and underwent Minimum Data Set (MDS) assessments at Mass General Brigham (MGB). Historical HER notes within the past year were utilized as model input. Altered mental status was defined based on the presence of C1310A Acute Mental Status Change or C1310 Signs and Symptoms of Delirium, including inattention, disorganized thinking, or altered level of consciousness. Three open‐source LLMs—QWEN2.5‐7B‐Instruct, Ministral‐8B‐Instruct, and Llama‐3.1‐8B‐Instruc were evaluated using two prompting strategies: a standard QA mode (direct answer output) and a Chain‐of‐Thought (CoT) mode (step‐by‐step reasoning). Performance was measured by accuracy using 1,000 bootstrapped iterations.ResultThe study included 779 patients (median age: 84 years, 44.2% male) and their corresponding 56,428 clinical notes from the past year, extracted from EHRs. Among the models, Llama‐3.1‐8B‐Instruct achieved the highest accuracy of 74.58% (95% CI: [74.48, 74.68]) in the standard QA mode. The CoT strategy improved reasoning transparency and interpretability but led to a slight performance decline compared to the standard QA approach.ConclusionThis study demonstrates the potential of using EHRs and LLMs to predict altered mental status in dementia patients. The LLMs showed fine predictive performance, while the CoT strategy provided enhanced interpretability at the cost of reduced accuracy. These findings emphasize the need to optimize LLM in clinical applications and highlight the promise of advanced AI tools in improving dementia care through efficient risk assessment and timely interventions.

Cultivation practices significantly impact soil functionality and quality, however, the effects of different cultivation durations remain inadequately quantified in the fragile karst ecosystems of southwestern China. This study employed a chronosequence approach to assess agricultural soil quality changes and identify their key drivers. Five land-use stages were selected and sampled: a natural reserve forest (NR) as a reference, recently burned land (0 a), and cultivated lands with durations of 1, 5, 15, and 30 years (1 a, 5 a, 15 a, 30 a, respectively) in the subtropical karst region of southwestern China. The soil quality index (SQI) was constructed using both the Total Data Set (TDS) and Minimum Data Set (MDS) methods. Principal component analysis (PCA) identified calcium (Ca), silt content and silicon (Si) as the key indicators within the MDS. Both TDS and MDS assessments revealed a progressive decline in SQI with increasing cultivation duration following slash-and-burn practices (R²=0.67 between TDS- and MDS-derived SQIs), indicating that agricultural activities significantly alter soil physicochemical properties and lead to soil degradation. The observed decrease in soil quality may be explained by two main mechanisms: (1) depletion of the soil-forming matrix in shallow karst environments; and (2) accelerated loss of acid-insoluble residues due to enhanced weathering under cultivation. These findings highlight the high vulnerability of karst soils to long-term farming, where inherently limited pedogenic materials and intense chemical weathering collectively exacerbate quality decline. This study provides critical insights for developing sus­tainable land management strategies in karst regions, empha­sizing the importance of practices that conserve soil fertility, reduce residue loss, and maintain essential ecosystem services. With the MDS model explaining 67% of the variance in the TDS-based SQI, it represents an efficient and practical tool for monitoring soil quality in fragile karst environments, thereby supporting evidence-based sustainable land use planning.

ObjectivesTo develop survey items for a national patient registry on Long COVID using a modified Delphi process.DesignThis study was based on a modified Delphi process involving three rounds of anonymous, online surveys to develop consensus on and prioritise survey elements to be included in a minimum dataset for use in a national patient registry in Canada. Initial Long COVID items were identified through an environmental scan of the literature.SettingThis study focused on healthcare systems in Canada and was conducted online.ParticipantsA panel of 52 experts (patients, caregivers, clinicians and researchers) participated in all three rounds of the online survey. These participants were recruited through the Long COVID Web network and word of mouth.ResultsIn total, 243 survey elements related to care, quality of life and symptoms were included in round 1 of the survey. 200 reached consensus and moved to round 2 with two additional elements being developed based on open-ended responses. In round 2, participants ranked these survey elements and 34 advanced. In round 3, 33 survey elements met the threshold of consensus with one added a priori. The 33 survey elements were then used to develop a Long COVID minimum dataset, which consists of 48 items.ConclusionsThe findings affirm broad consensus for collecting data related to fatigue, post-exertional malaise, cardiovascular issues, respiratory problems and cognitive issues. This highlighted the desire for quality-of-life indicators and information related to care utilisation, quality and access.

Standardising socio-demographic data collection in pain research: Introducing consensus recommendations for a minimum dataset.

Frailty Status and Dysphagia Trajectory Among Hospitalized Nursing Home Residents With Advanced Dementia.

Alignment of Minimum Data Set 3.0 Ratings With Staff Care Partner Reports of Symptoms of Distress for Nursing Home Residents.

Turnover in Nursing Homes That Serve Residents With Serious Mental Illness.

Care Trajectories for a Cohort of First-Time Nursing Home Admissions: A 5-Year Longitudinal Study in Minnesota.

To evaluate the actual effects of different ecological restoration technologies on high and steep rock slopes in the dry-hot valley area, we compared three typical slope ecological restoration techniques, vegetation concrete (VC), soilless spraying (SPF), and vegetation trough (VS) on the high and steep rock slope of Baihetan Hydropower Station. We conducted a one-year monitoring of soil physical and chemical indicators and vegetation characteristics from January to December 2022, and calculated the ecological restoration index (ERI) using the minimum dataset method, which were used to comprehensively evaluate the ecological restoration effects of each technique. The results showed that: 1) During the maintenance monitoring period, soil physical characteristics (bulk density, porosity, moisture content), soil organic matter, and nutrient (total nitrogen, total phosphorus, available phosphorus) contents of the three remediation techniques showed a fluctuating trend with seasons. Plant characteristics (plant height, plant diameter, vegetation coverage coefficient, aboveground biomass) increased from 3.6-9.3 cm, 0.98-2.16 mm, 0.12-0.61, and 42.80-163.56 g·m-2 to 11.5-14.7 cm, 2.85-4.05 mm, 0.68-0.98, and 368.00-421.12 g·m-2, respectively, while cation exchange capacity increased from 6.13-13.94 cmol·kg-1 to 13.94-20.42 cmol·kg-1. Soil pH decreased from 7.56-8.05 to 7.17-7.51. VC was generally superior to SPF and VS in enhancing soil structure and plant growth. 2) The minimum dataset consisted of plant height, available phosphorus, vegetation coverage, bulk density, and total nitrogen, which were significantly positively correlated with the entire dataset (R2=0.733) and could effectively replace the entire dataset for ecological restoration evaluation. 3) The restoration process of slopes presented a restoration path of "soil matrix construction plant growth and reproduction". The contribution rate of soil ERI of the three restoration techniques in spring was 66.3%-70.5%, that in summer was 43.7%-58.4%, with the contribution rate of vegetation to ERI being 41.6%-56.3%. The contribution rate of vegetation to ERI in autumn and winter exceeded that of soil, ranging from 54.7% to 64.1% and 55.6% to 61.0%, respectively. 4) The annual average ERI values of three typical slope ecological restoration techniques were ranked as VC (0.576)>SPF (0.549)>VS (0.452), and the final values showed the same trend (0.676>0.639>0.538), indicating that VC had the best ecological restoration effect.

Public health at multi-day youth-focused mass gathering events: a scoping review of the evidence.

Temporal trends in sepsis hospitalisations and mortality in Aotearoa New Zealand, 2000–2019: a population-based study

This study aims to analyze land use change and its impact on soil quality in the Catchment Area of the Manten Sub-watershed, Malang Regency. Land use changes were assessed using satellite imagery data from 1998, 2008, 2018, and 2024, while soil physical and chemical properties were measured to develop the Soil Quality Index (SQI). The SQI was computed using the Minimum Data Set (MDS) approach based on principal component analysis, and each indicator was scored and integrated using a weighted additive formula. The results show a significant increase in built-up areas by 12% and a decrease in plantation land due to land conversion. These changes were driven by population growth and urbanization. The highest SQI value was recorded in plantation areas (0.70), while the lowest was found in dryland agriculture (0.58). The decline in soil quality was mainly caused by low organic matter content and unsustainable land management practices. This study highlights the importance of land use planning based on soil potential to support environmental sustainability.

As the most extensive terrestrial ecosystem, grassland exhibits substantial ecological functions and scientific research significance. Conducting a scientific assessment of the soil fertility of grasslands is of paramount importance for attaining sustainable grassland management, especially for the Tibetan Plateau, which has the most vulnerable ecosystem. This study endeavors to evaluate the soil fertility and spatial differentiation patterns of the natural grasslands in the Tibetan Plateau. Initially, we developed a Soil Fertility Evaluation Index (SFEI) for natural grasslands by integrating three representative soil indicators (total nitrogen, soil organic matter, and bulk density) and a vegetation indicator (fractional vegetation cover). The selection of these indicators followed the Minimum Data Set (MDS) principle, ensuring both ecological relevance and consistent data availability across all sampling plots in the Tibetan Plateau. Subsequently, validation based on field sampling data showed an overall accuracy of 69.89%. Moreover, the evaluation result revealed a clear eastward-increasing gradient in soil fertility, with low fertility in the western regions (e.g., Ngari and Nagqu) and medium-to-high fertility in the central and eastern regions (e.g., Lhasa, Yushu, and Golog), consistent with regional hydrothermal patterns. The proposed method offers a novel and practical framework for assessing soil fertility of natural grassland in the Tibetan Plateau, with significant implications for differentiated grassland management and ecological restoration.

Gabapentinoid-related peripheral edema may prompt loop diuretic prescribing. Nursing home (NH) residents may be especially prone to this prescribing cascade. We estimated the incidence and identified predictors of the gabapentinoid-loop diuretic prescribing cascade in NHs. We conducted a retrospective cohort study using 2016-2022 Medicare claims linked with Minimum Data Set assessments. We identified residents aged ≥ 66 years who initiated gabapentinoids in NHs and who had no evidence of loop diuretic use, heart failure, or renal insufficiency during the prior 6 months. The outcome was loop diuretic initiation within 90 days of gabapentinoid initiation. Using multivariable Poisson regression models, we estimated adjusted risk ratios (aRR) with 95% robust confidence intervals to identify predictors. We used pooled logistic regression models to examine the relationship between time-varying gabapentinoid dose and loop diuretic initiation risk. Among 23,544 residents, 994 (4.2%) experienced a prescribing cascade at a median of 36 days (IQR 15-61) after gabapentinoid initiation. Risk was higher with age 86-90 years (aRR = 1.60) or ≥ 91 years (aRR = 1.38); a diagnosis of chronic pain or fibromyalgia (aRR = 1.16), or diabetes (aRR = 1.23); and receipt of potassium-sparing diuretics (aRR = 1.53), thiazide diuretics (aRR = 1.27), or 15 or more unique medications (aRR = 1.18). Higher (versus lower) weekly gabapentin dose during follow-up was associated with a 1.45 times higher prescribing cascade risk over 13 weeks. Those with Alzheimer's Disease and Related Dementias (aRR = 0.79), or moderate (aRR = 0.72) to severe cognitive impairment (aRR = 0.59) had a lower risk versus those with intact cognition. Approximately 1 in 20 NH residents who initiate gabapentinoids receives a loop diuretic within 3 months. Potentially modifiable predictors included existing polypharmacy and titrating gabapentinoid doses. NH clinicians should monitor for edema soon after gabapentinoid initiation and consider dose reductions or discontinuation before adding a loop diuretic.

Desertification erodes arable land and human habitats. Vegetation restoration represents a critical process for improving the quality of sandy land by enhancing soil structure and nutrient cycling. This study aims to investigation how vegetation restoration affects soil physicochemical properties and soil quality. Five vegetated land types were selected (Pinus sylvestris var. mongholica Litv., PS; Amygdalus pedunculata Pall., AP; Salix psammophila, SP; Amorpha fruticosa L., AF; Artemisia desertorum Spreng., AD). Bare sandy land (BS) served as the control. The physicochemical properties of 270 soil samples from three vertical depth intervals (0–10, 10–20, and 20–30 cm) were analyzed. The findings demonstrated that vegetation restoration markedly improved the proportion of finer soil particles (clay and silt) and organic carbon, while the variations in total phosphorus, ammonia nitrogen, and nitrate nitrogen were not significant. To better understand the variations in soil quality in different vegetated lands, a soil quality index (SQI) was developed that considers multiple soil physical and chemical indicator selections and scoring methods. The SQI based on the minimum dataset and linear scoring method better differentiated the soil quality for sandy lands and showed higher values for SP among all five vegetated lands and BS. Improvements in soil quality were closely related to vegetation properties (density and coverage) and litter characteristics (thickness, water content, and total phosphorus content). Restoration strategies for sandy lands should focus more strongly on species selection, taking into account interspecific variations in litter production, physicochemical properties, canopy architecture, and planting density to more effectively improve soil quality.

Abstract Background Randomised controlled trials (RCTs) in facelift surgery remain few and variably reported; transparent, reproducible methods are essential to interpret efficacy and safety. Objectives To assess the methodological quality and reporting standards of facelift RCTs using the Consolidated Standards of Reporting Trials (CONSORT) and CONSORT-NPT guidelines, identifying patterns of adherence and areas for improvement. Methods We conducted a PRISMA-guided systematic review of RCTs evaluating facelift techniques or perioperative strategies. RCTs focusing on facelift techniques were included based on study design and relevance. Adherence to the CONSORT 2010 or CONSORT-NPT 2017 checklist was retrospectively assessed for each included study. Risk of bias was assessed using the Cochrane RoB 2.0 tool, and evidence quality was appraised via GRADE. Results Ten RCTs (n=457; mean sample 46) met inclusion. Mean CONSORT adherence was 56%, with high for intervention description and statistical analysis (both 100%) but poor for tailored interventions (10%), trial registration (20%), and trial protocol (30%). Adherence showed weak correlations with journal impact factor (R²=0.0024) and author count (R²=0.171). Only 3 trials were low risk of bias; GRADE certainty was largely low-moderate, limited by imprecision and suspected publication bias. Conclusions Facelift RCTs show variable, often suboptimal reporting, leaving the evidence base thin despite rising demand. Strengthening trial quality requires field-wide pre-registration and protocol publication, validated outcome measures, and consistent CONSORT enforcement; a standardized minimum dataset and registry-based reporting would further bolster evidence for facial rejuvenation surgery.