Matching Rate Research Articles

A new method for individual treetop detection with low-resolution aerial laser scanned data

In the past decade, the use of three-dimensional forest information from airborne Light Detection and Ranging (LiDAR) has become widespread in forest inventories. Accurate Individual Treetop Detection (ITD) and crown boundary delineation using LiDAR data are critical for obtaining precise inventory metrics. To address this need, we introduced a novel growing tree region (GTR)-driven ITD method that utilizes canopy height models (CHM) derived from very low-resolution airborne LiDAR data. The GTR algorithm consists of three key stages: (i) preserving all height layers through incremental cutting and stacking of CHM; (ii) employing a three-layer concept to identify individual treetops; and (iii) refining the detected treetops using a distance-based filter. Our method was tested in five temperate forests across Central Europe and was compared against the widely-used local maxima (LM) search combined with an optimized variable window filtering (VWF) technique. Our results showed that the GTR method outperformed LM with VWF, particularly in forests with high canopy density. The achieved root mean square accuracies were 74% for the matching rate, 19% for commission errors, and 27% for omission errors. In comparison, the LM with the VWF method resulted in a matching rate of 71%, commission errors of 20%, and omission errors of 31%. To facilitate the application of our algorithm, we developed an R package called TREETOPS, which seamlessly integrates with the lidR package, ensuring compatibility with existing treetop-based segmentation methods. By introducing TREETOPS, we provide the most accurate open-source tool for detecting treetops using low-resolution LiDAR-derived CHM.

Vehicle Seat Adjustment System based on Body Image Recognition

A vehicle seat adjustment system based on body image recognition is designed. The system uses body image recognition algorithms to calculate the physique parameters of passengers, and automatically adjusts the vehicle seat position. Through on-site testing, the matching rate between the automatically adjusted vehicle seat position and passenger body shape reaches 98.1%, which can effectively improve the convenience and comfort of the vehicle.

Extreme Resonant Eccentricity Excitation of Stars around Merging Black-Hole Binary.

We study the dynamics of a star orbiting a merging black-hole binary (BHB) in a coplanar triple configuration. During the BHB's orbital decay, the system can be driven across the apsidal precession resonance, where the apsidal precession rate of the stellar orbit matches that of the inner BHB. As a result, the system gets captured into a state of resonance advection until the merger of the BHB, leading to extreme eccentricity growth of the stellar orbit. This resonance advection occurs when the inner binary has a nonzero eccentricity and unequal masses. The resonant driving of the stellar eccentricity can significantly alter the hardening rate of the inner BHB and produce observational signatures to uncover the presence of nearby merging or merged BHBs.

Effect of a novel artificial intelligence (AI) –enabled multi-trial matching system on patient matching using real-world data.

e13501 Background: Currently clinical research teams at sites are manually matching patients to clinical trials, where each trial in our experience takes at least 25 minutes using highly trained dedicated resources. This limits cancer patients’ clinical trial options and enrollment rates. Moreover, the lack of NGS testing results may also negatively impact matching to clinical trials in targeted therapies. PCC, a consortium of 7 of the Top 20 Pharmaceutical companies, collaborated with Massive Bio to demonstrate how cancer patients’ matching rates can be increased if patients are prescreened to multiple trials simultaneously (i.e. multi-trial matching) instead of a single trial and NGS testing results are integrated into the matching process. Methods: Massive Bio has developed a first-in-class AI-enabled matching system to extract 180 structured clinical data from medical records using computer vision and natural language processing and a recommendation system for matching patients to parameterized inclusion/exclusion (I/E) criteria from over 14,000 actively recruiting interventional clinical trials.Our analysis included a sample of 5,707 cancer patients from the Massive Bio Synergy AI real-world database, corresponding to tumor types in 23 selected trials. We developed a decision-tree algorithm for retrospective matching analysis using modeled I/E criteria and compared these results to the theoretical matching rate (TMR) for each trial based on tumor type and biomarker prevalence, extent of disease at diagnosis and prior treatment history. Results: 690 patients matched to at least one trial were matched to 1.82 times more trials in multi-trial matching; 2.10 times more potential matches were obtained with hypothetical 100% NGS testing; and 99.9% matching time was reduced with AI enabled system; key findings are summarized (Table). Conclusions: This study confirms the underutilization of NGS technology and substantial benefits of AI and NGS in cancer trial matching. Their use resulted in a nearly two-fold increase in potential patient eligibility for trials, and an unprecedented reduction in manual effort, underscoring a significant advancement in the efficiency of the clinical trial matching process. A prospective country-wide analysis is under consideration. [Table: see text]

Fueling the future of neurosurgery: increasing trends in female enrollment expanding the neurosurgical workforce from 2010 to 2019.

Regular reporting on recruitment of women into neurosurgery is a mechanism to track the progress of minority inclusion and diversity. For more than 20 years, half of US medical students have been women, yet a disproportionate minority pursue a career in neurosurgery. The authors previously published data tracking women in neurosurgery for the prior decade from 2000 to 2009, which showed an increasing match rate of women over time, from 18 women matched at the beginning of the decade to 36 matched at the end. They now present updated data on the matriculation of women into neurosurgical residencies across the most recent decade, from 2010 to 2019. Public databases from the Association of American Medical Colleges were analyzed for applicants to neurosurgical residency from 2010 to 2019. Demographic data for those matched was obtained from the American Medical Association Masterfile and analyzed for year of match and gender. Secondary outcomes included program location, age, and gap year(s) between medical school and residency. Women made up 18.8% (394/2094) of residents matched into neurosurgery from 2010 to 2019, compared to 12% during the previous decade. There was an increase in overall match rate of 2.2% per year (male + female) from 2010 to 2019, representing the predicted need for expansion of the neurosurgical workforce to meet national needs. Women made up the majority of this expansion, rising by 5.5% over the time period (p = 0.002), compared to men increasing by 1.4% over the time period (p = 0.096). The percentage of women applicants who matched was 34.4%, compared to men at 38.6% (p = 0.009). There was no significant difference in gender with regard to age, number of gap years, or program location. The number of women matching into neurosurgery has continued to increase across the last 2 decades. Despite this, there remains significant disparity between the number of men and women matching into neurosurgery. Ongoing mitigation of barriers to recruitment of women into neurosurgery is critical. The authors' data highlight the fact that women are important to fueling the expansion of the neurosurgical workforce.

Exploring the Feasibility of GPT-4 as a Data Extraction Tool for Renal Surgery Operative Notes.

GPT-4 is a large language model with potential for multiple applications in urology. Our study sought to evaluate GPT-4's performance in data extraction from renal surgery operative notes. GPT-4 was queried to extract information on laterality, surgery, approach, estimated blood loss, and ischemia time from deidentified operative notes. Match rates were determined by the number of "matched" data points between GPT-4 and human-curated extraction. Accuracy rates were calculated after manually reviewing "not matched" data points. Cohen's kappa and the intraclass coefficient were used to evaluate interrater agreement/reliability. Our cohort consisted of 1498 renal surgeries from 2003 to 2023. Match rates were high for laterality (94.4%), surgery (92.5%), and approach (89.4%), but lower for estimated blood loss (77.1%) and ischemia time (25.6%). GPT-4 was more accurate for estimated blood loss (90.3% vs 85.5% human curated) and similarly accurate for laterality (95.2% vs 95.3% human curated). Human-curated accuracy rates were higher for surgery (99.3% vs 93% GPT-4), approach (97.9% vs 90.8% GPT-4), and ischemia time (95.6% vs 30.7% GPT-4). Cohen's kappa was 0.96 for laterality, 0.83 for approach, and 0.71 for surgery. The intraclass coefficient was 0.62 for estimated blood loss and 0.09 for ischemia time. Match and accuracy rates were higher for categorical variables. GPT-4 data extraction was particularly error prone for variables with heterogenous documentation styles. The role of a standard operative template to aid data extraction will be explored in the future. GPT-4 can be utilized as a helpful and efficient data extraction tool with manual feedback.

Crater Triangle Matching Algorithm Based on Fused Geometric and Regional Features

Craters are regarded as significant navigation landmarks during the descent and landing process in small body exploration missions for their universality. Recognizing and matching craters is a crucial prerequisite for visual and LIDAR-based navigation tasks. Compared to traditional algorithms, deep learning-based crater detection algorithms can achieve a higher recognition rate. However, matching crater detection results under various image transformations still poses challenges. To address the problem, a composite feature-matching algorithm that combines geometric descriptors and region descriptors (extracting normalized region pixel gradient features as feature vectors) is proposed. First, the geometric configuration map is constructed based on the crater detection results. Then, geometric descriptors and region descriptors are established within each feature primitive of the map. Subsequently, taking the salience of geometric features into consideration, composite feature descriptors with scale, rotation, and illumination invariance are generated through fusion geometric and region descriptors. Finally, descriptor matching is accomplished by computing the relative distances between descriptors and adhering to the nearest neighbor principle. Experimental results show that the composite feature descriptor proposed in this paper has better matching performance than only using shape descriptors or region descriptors, and can achieve a more than 90% correct matching rate, which can provide technical support for the small body visual navigation task.

Interlaboratory study on real-time PCR detection and quantification of the European anglerfish, pike, and seabream parvalbumin gene

This study presents a large-scale interlaboratory comparison (ILC) aimed at detecting and quantifying DNA from two European anglerfish (Lophius budegassa, Lophius piscatorius), pike (Esox lucius) and sea bream (Spondyliosoma cantharus) using real-time qPCR. To detect amplification of the parvalbumin genetic marker, single and multiplex qPCR assays using EvaGreen® dye or TaqMan™ probes were used. Genomic DNA isolated from target fish species and an advanced DNA calibrator, gBlocks® gene fragments, were used as standards. The DNA of anglerfish, pike and sea bream as well as their mixtures were analysed together with 14 other non-target fish species. All target fish samples were correctly identified by the participating laboratories. Qualitative assessment of anglerfish and seabream DNA showed an accuracy rate of 100%, while pike DNA achieved a match rate of 99%. Validation of quantitative protocols in four different laboratories consistently achieved z-scores below 2, indicating satisfactory performance and confirming the high degree of similarity of laboratory results. Furthermore, high accuracy and efficiency were demonstrated for the quantification of anglerfish and seabream DNA by triplex qPCR using TaqMan™ probes. Regarding the selected gene marker, the major fish allergenic protein parvalbumin enables indirect detection and quantification of the allergen in the sample. Therefore, the use of proposed protocols can significantly contribute to protecting the health of consumers and to controlling the food market.Graphical abstract

Respiratory motion tracking of the thoracoabdominal surface based on defect-aware point cloud registration.

The performance of conventional lung puncture surgery is a complex undertaking due to the surgeon's reliance on visual assessment of respiratory conditions and the manual execution of the technique while the patient maintains breath-holding. However, the failure to correctly perform a puncture technique can lead to negative outcomes, such as the development of sores and pneumothorax. In this work, we proposed a novel approach for monitoring respiratory motion by utilizing defect-aware point cloud registration and descriptor computation. Through a thorough examination of the attributes of the inputs, we suggest the incorporation of a defect detection branch into the registration network. Additionally, we developed two modules with the aim of augmenting the quality of the extracted features. A coarse-to-fine respiratory phase recognition approach based on descriptor computation is devised for the respiratory motion tracking. The efficacy of the suggested registration method is demonstrated through experimental findings conducted on both publicly accessible datasets and thoracoabdominal point cloud datasets. We obtained state-of-the-art registration results on ModelNet40 datasets, with 1.584∘ on rotation mean absolute error and 0.016 mm on translation mean absolute error, respectively. The experimental findings conducted on a thoracoabdominal point cloud dataset indicate that our method exhibits efficacy and efficiency, achieving a frame matching rate of 2 frames per second and a phase recognition accuracy of 96.3%. This allows identifying matching frames from template point clouds that display different parts of a patient's thoracoabdominal surface while breathing regularly to distinguish breathing stages and track breathing.

Evaluation of the Impact of ChatGPT on the Selection of Surgical Technique in Bariatric Surgery.

With the growing interest in artificial intelligence (AI) applications in medicine, this study explores ChatGPT's potential to influence surgical technique selection in metabolic and bariatric surgery (MBS), contrasting AI recommendations with established clinical guidelines and expert consensus. Conducting a single-center retrospective analysis, the study involved 161 patients who underwent MBS between January 2022 and December 2023. ChatGPT4 was used to analyze patient data, including demographics, pathological history, and BMI, to recommend the most suitable surgical technique. These AI recommendations were then compared with the hospital's algorithm-based decisions. ChatGPT recommended Roux-en-Y gastric bypass in over half of the cases. However, a significant difference was observed between AI suggestions and actual surgical techniques applied, with only a 34.16% match rate. Further analysis revealed any significant correlation between ChatGPT recommendations and the established surgical algorithm. Despite ChatGPT's ability to process and analyze large datasets, its recommendations for MBS techniques do not align closely with those determined by expert surgical teams using a high success rate algorithm. Consequently, the study concludes that ChatGPT4 should not replace expert consultation in selecting MBS techniques.

Pilot Study: Developing Curriculum to Prepare Fourth-Year Medical Students Applying for OBGYN Residency Within the Defense Health Agency.

Obstetrics and gynecology (OBGYN) is becoming increasingly competitive among medical specialties. As a result, many medical schools have frameworks to help their students increase their competitiveness to maximize chances of a successful match. However, "boot camps" have traditionally been geared toward the transition to intern year and not to sub-internships during the fourth year of medical school. We aimed to develop a boot camp for rising fourth-year medical students planning to apply into OBGYN before the initiation of their sub-internships and interview season. Sixteen students enrolled in this course led by faculty OBGYNs at our institution. We utilized a combination of mentorship, coaching, and education to achieve applicant success. The curriculum included formal lectures, surgical skills training, and focused feedback from our applicant development committee. Our response rate was 100% and 94% for the pre-intervention and post-intervention survey, respectively. Before the intervention, the majority of participants felt either neutral (47.4%) or unprepared (21.1%) regarding their knowledge before their sub-internships. However, after the intervention, all reported feeling more prepared for their rotations. All respondents either agreed (60%) or strongly agreed (40%) that the course increased their confidence in performing well on audition rotations. Our match rate increased from 62% for the class of 2022 (before this intervention) to 75% for the class of 2023. In light of the increasing residency match competitiveness, it is crucial to investigate programs that can assist students in developing stronger applications. This reproducible intervention utilizes few resources and can be implemented at most medical schools to better support their OBGYN applicants.

Matching UAV-Based Simulated High-Resolution SAR and Real SAR Images

Abstract. Our experiment focuses on generating simulated high-resolution (HR) Synthetic Aperture Radar (SAR) images from the photogrammetric 3D model derived from Unmanned Aerial Vehicle (UAV) imagery. The goal is to match these simulated images with real SAR data of complex scenes containing vegetation, buildings, roads, and the construction of receiving antennas. Simulated SAR reflectivity map was based on ray tracing techniques.Two feature matching algorithms, namely Scale-Invariant Feature Transform (SIFT) and Oriented FAST and Rotated BRIEF (ORB) were employed for co-registration of the simulated to the real SAR images. The effectiveness of these algorithms is evaluated by their matching rates. The experiment results of the matching process highlight the superior performance of ORB over SIFT, attributed to its robustness against rotation variations and noise. ORB emerges as a versatile choice for high-resolution real and simulated SAR registration tasks.

Multi-time scale economic regulation model of virtual power plant considering multiple uncertainties of source, load and storag

A novel multi-stage time scale economic dispatch scheme is proposed for virtual power plants, taking into account the uncertainties arising from the connection of distribution network sources. This research introduces specific scheduling schemes tailored to various time scales within distribution networks, including a fuzzy optimized day ahead scheduling scheme, an intra-day scheduling scheme combined with Deep Q Network, and an adaptive optimized real-time scheduling scheme. This plan mainly considers the impact of photovoltaic output and conducts scheduling one day in advance through fuzzy optimization. In the intraday scheduling, different strategies were adopted in the study. By combining with Deep Q Network, research on scheduling for intraday demand within the power system. The analysis is conducted through rigorous modeling. Experimental tests were conducted to evaluate the performance of the proposed schemes. The day ahead dispatching primarily considers the impact of photovoltaic output and calculates the cost associated with each link in the grid under three different meteorological conditions. In the intra-day scheduling, the total costs for Scenario 1, Scenario 2, and Scenario 3 are found to be 34,724.5 yuan, 36,296.5 yuan, and 33,275.8 yuan, respectively. Notably, strategies 1 and 2 demonstrate lower costs compared to the pre-day scheduling, with the exception of Scenario 3. In real-time scheduling, considering the matching between sources and sources, the matching rate between sources and sources can be maintained at over 95%, and the stability and cost of the power grid have significantly decreased. In summary, by proposing a multi-stage time scale economic scheduling scheme, this study fully considers the uncertainty of the power supply of the distribution network access, as well as the different needs of day, day and real-time scheduling, providing an effective solution for the power dispatching of virtual power plants and providing important technical support for the reliability and economy of the power system.

Geographic Proximity of Medical Students to PM&R Residency Programs Is Associated With Increased Match Rates Into PM&R.

Despite the growing popularity of physical medicine and rehabilitation as a specialty among medical students, meaningful experiences and mentorship can be challenging to obtain and may significantly vary depending on opportunities available to interact with physiatrists. This study explores the association between the geographic proximity of physical medicine and rehabilitation residency programs to medical schools and the match rate of medical students into physical medicine and rehabilitation from 2019 to 2021. Data on US medical schools, graduates, and physical medicine and rehabilitation residency programs were collected from publicly available sources, and a sample of 1193 physical medicine and rehabilitation residents from US medical schools was analyzed using a one-sample proportion test. The proportion of physical medicine and rehabilitation residents originating from medical schools with physical medicine and rehabilitation residency programs in the same metropolitan area was significantly greater than the corresponding proportion of expected residents based on medical school graduates, even when controlling for medical school affiliations with physical medicine and rehabilitation residency programs. These findings suggest that exposure and opportunities provided by physical medicine and rehabilitation residency programs may influence nearby medical students and that expanding residency programs into geographic regions without existing physical medicine and rehabilitation programs may foster interest and promote growth in the field of physiatry.

Building a Physician-Scientist Pathway for Underrepresented Medical Students Through a Summer Research Experience.

There is a need to increase the number of physician-scientists from underrepresented in medicine (URiM) groups. To engage URiM medical students, a committee of pediatric departmental leaders at the Children's Hospital of Philadelphia created the Summer Underrepresented in Medicine Medical Student Research program. This 8-week, onsite research and clinical experience takes place during the summer between students' first and second years of medical school. Applications were solicited between 2019-2023 through nationwide outreach to medical school deans and members of URiM student organizations. Accepted students were assigned a mentor to lead their research and clinical exposure. A curriculum highlighting aspects of academic medicine was developed. Students received a $3,000-$5,500 stipend for in-person participation. In 2020 and 2021, adjustments were made (e.g., virtual programming) to avoid interruptions during the COVID-19 pandemic. In the 2019-2023 application cycles, 298 students applied. Of 128 students who participated, 78 (61%) completed a postprogram survey. Students' survey feedback was positive. They indicated the program met expectations (mean rating = 1.3; scale: 1 = strongly agree to 5 = strongly disagree). Students reported they learned valuable information/skills (mean = 1.3) and that participation was worth time spent away from other responsibilities (mean = 1.3). The 2019 cohort (n = 12) achieved a 100% residency match rate. In addition, 4 (33%) of these students reported they are obtaining additional degrees or are performing research. After the program, many mentors continued to include students in their research projects. Next steps include incorporating a standardized, scored rubric for selecting applicants; adding 3 lead mentors, an executive committee, and a faculty advisory board; establishing earlier pathway programming (e.g., at elementary and middle school levels); continuing to track/support alumni throughout their careers; and pursuing federal funding to expand the program.

The Current State of Advanced Pediatric Emergency Medicine Point-of-Care Ultrasound (POCUS) Training: Exploring Recent POCUS Fellowship Application Trends and Alternate Training Models.

This study aims to assess the current state of advanced pediatric emergency medicine (PEM) point-of-care ultrasound (POCUS) training in North America, including trends in dedicated PEM POCUS fellowships and alternative advanced POCUS training pathways, to better guide future educational efforts within the field. We identified and surveyed 22 PEM POCUS fellowship directors across the United States and Canada regarding PEM POCUS fellowship application trends, potential barriers to pursuing additional POCUS training, and novel training models that meet the needs of the PEM POCUS workforce. The past 5 years have seen a growth in both PEM POCUS fellowship program number and trainee positions available, with a general impression by fellowship directors of a high demand for faculty who have these training credentials. However, there was a discordant drop in fellowship applicants and corresponding match rate in 2022, the cause of which is not clear. A number of programs are offering alternative advanced training options including combined PEM/POCUS fellowships and POCUS tracks within PEM fellowship. As POCUS use within PEM evolves, a growing number of advanced training options are being developed. Understanding the motivations and barriers for pursuing advanced POCUS training can help to shape these options going forward, to ensure the experience incorporated within each model meets the needs of trainees, the needs of PEM divisions, and the future needs of our field.

Investigating the clinical reasoning abilities of large language model GPT-4: an analysis of postoperative complications from renal surgeries

PurposeLarge language models, a subset of artificial intelligence, have immense potential to support human tasks. The role of these models in science and medicine is unclear, requiring strong critical thinking and analysis skills. The objective of our study was to evaluate GPT-4's abilities to assess postoperative complications after renal surgeries. Materials and methodsDischarge summaries were compiled, and patient information was deidentified in a Python-based program. Prompts were engineered in GPT-4 to assess for the presence of postoperative complications. GPT-4 was further asked to interpret each complication's Clavien-Dindo classification and institutional-specific category. GPT-4's database was compared to a human-curated database. Discrepancies were manually reviewed to calculate match and accuracy rates. ResultsApproximately 944 renal surgeries were conducted from August 2005 to March 2022. There was a 79.6% match rate between GPT-4 and human-curated data in detecting postoperative complications. Accuracy rates were 86.7% for GPT-4 and 92.9% for human-curated. A subgroup of 139 patients had a complication detected by both GPT-4 and human with available Clavien-Dindo classification and category information. There was a 37.4% overall match rate for Clavien-Dindo grade and 55.4% match rate for category. ConclusionsGPT-4 was able to accurately detect if there were any postoperative complications. It struggled with the complex task of further analyzing complications, especially with Clavien-Dindo classification, which requires more critical thinking and interpretation. While GPT-4 is not yet ready for advanced postoperative complication analysis, it can still be used to support clinicians in this endeavor.

BM-RCWTSG: An integrated matching framework for electric vehicle ride-hailing services under stochastic guidance

Electric vehicles (EVs) are playing an increasingly vital role in sustainable ride-hailing services, primarily due to their potential to reduce carbon emissions and enhance environmental well-being. A core service of ride-hailing platforms is to match riders with nearby drivers within a short time window. In this mandate, it is crucial to ensure a high matching rate and low rider waiting time since these are key indicators of customer satisfaction and the platform’s operational effectiveness. However, existing matching mechanisms in the literature failed to consider important aspects related to EVs, including the range limit, charging detour time, and charging waiting time. These factors can significantly affect the system matching rate, rider waiting time, and vehicle utilization rate. To address the new challenges of the increased adoption of EVs in ride-hailing services, we propose a data-driven stochastic optimization framework for EV ride-hailing matching. The proposed framework integrates two stochastic optimization models, namely the stochastic guidance (SG) model and the EV matching (EVM) model. The SG model optimizes EV relocation and improves the system matching rate by proactively guiding idle EVs to the designated regions based on probabilistic rider demand forecasting. The EVM model utilizes the relocated EVs and current rider demands to provide the optimal matching solution that minimizes the overall cost associated with rider waiting time, EV charging waiting time, and charging detours. We conduct experimental validation of this approach using two real-world data sets from New York City. Comparative results suggest that by utilizing the proposed framework, the matching rate can be increased up to 22.9%, and the rider and EV waiting times can be reduced up to 14.9% and 73.6%, respectively, compared to the benchmark models.

Abstract PO3-12-06: The pilot study for a mobile-based monitoring solution to manage menopausal symptoms of premenopausal breast cancer patients with endocrine therapy

Abstract Background Endocrine therapy (ET) is the most important treatment for hormone receptor (HR)-positive breast cancer (BC) patients. Each of the endocrine medications, such as aromatase inhibitors (AIs), luteinizing hormone-releasing hormone (LHRH) analogs, and selective estrogen receptor modulators, has its own set of side effects, and these might lead to poor drug adherence and eventual discontinuation. There are few systematic monitoring systems for menopausal symptoms of patients on ET. We conducted a prospective, pilot study through the mobile platform-based monitoring solution which contains a questionnaire about menopausal symptoms to assess the feasibility of the solution. Methods We screened and collected data from HR-positive BC patients who underwent surgery at a single institution from May 2022, until recruiting a total of 20 patients. Patients scheduled for postoperative ET at age 50 or younger were included. Preoperatively confirmed metastatic BC patients were excluded. A total of 19 patients were enrolled, excluding one patient who self-discontinued medication. Menopause Rating Scale (MRS) surveys were periodically administered to the patients using a mobile platform-based solution to collect responses during first 3 months of ET. The primary endpoint of this study was the response rate of the patients, and the second endpoint was MRS scores and patient satisfaction. At the time of initial enrollment before the start of ET, MRS was collected using a paper questionnaire. The concordance rate of MRS response using a paper questionnaire and this solution was compared. We also conducted a user evaluation at the end of the third month of ET to determine satisfaction. The data collected from the solution were used by physicians to assist in patient care for clinical use. Results The median age of the cohort was 39 (range 29-47). Six patients were treated with Tamoxifen alone, Tamoxifen with LHRH analog in 12 patients, and AI with LHRH analog in 1 patient. There were 3 (15.8%) of pTis, 13 (68.4%) of pT1, and 3 (15.8%) of pT2, as well as 18 (94.7%) of pN0 and 1 (5.3%) patients of pN1. The total response rate of patients to the solution during the study period was 84.5%. Each symptom has different response rates and MRS scores, from 70.2% for sex-related symptoms to 95.4% for joint pain, and from 1.24 out of 4 for sleep disturbances to 0.45 out of 4 for vaginal dryness. The match rate between mobile and paper surveys was 90.0%. The overall satisfaction score for the solution of patients was 8.06 out of 10, with the highest score (8.79 out of 10) given in terms of using patients’ responses in outpatient appointments with the physician. Conclusion The analysis of MRS using this mobile solution in premenopausal BC patients undergoing postoperative ET is expected to be feasible for clinical use and should be analyzed in a large-scale study in the future. Citation Format: Dong Seung Shin, Jai Min Ryu, Byung Joo Chae, Jeong Eon Lee, Seok Won Kim, Seok Jin Nam, Jonghan Yu. The pilot study for a mobile-based monitoring solution to manage menopausal symptoms of premenopausal breast cancer patients with endocrine therapy [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-12-06.

A 15-Year Analysis of Supply and Demand for Hematology and Oncology Training in the United States.

There is a paucity of research on the supply of the hematology and oncology workforce despite projected shortages in the United States Over the past 15 years of the hematology and oncology match (HOM), we hypothesized that there would be more growth in the number of training positions relative to applicants, higher match rates for US allopathic graduates relative to non-US allopathic graduates, and fewer applicants matching at their top fellowship choices. This was a national, retrospective cohort study of all applicants in the HOM (2009-2023). Match rates and applicant-to-training position ratios were calculated and compared over time with Pearson tests. Growth in the number of annual training positions (426-708; 66% increase) exceeded growth in the number of interested applicants (706-945; 34% increase; P < .001). Annual applicant-to-training position ratios decreased from 1.7 to 1.3 (r = -0.813; P < .001). Match rates increased over the study period for both US allopathic graduates (79%-88%; r = 0.761; P = .001) and non-US allopathic graduates (45%-63%; r = 0.801; P < .001). During each year, match rates for US allopathic graduates exceeded those for non-US allopathic graduates (P < .001). From 2018 to 2023, US allopathic graduates (83%) had higher match rates than US osteopathic graduates (60%) and international medical graduates (50%; P < .001). The percentage of applicants that matched at one of their top three fellowship choices increased from 53% to 60% (r = 0.480; P = .070). Fewer available annual training positions went unfilled over the study period (3%-0.3%; r = - 0.870; P < .001). Match rates have increased in the HOM but remain competitive especially for non-US allopathic graduates. Future investigation is needed to understand disparities in match outcomes by additional applicant and fellowship program characteristics. Ongoing surveillance of HOM outcomes remains critical given the projected shortages in the US hematology and oncology workforce.