"Digital Phenotyping of Neuromuscular\u2013Cognitive Aging Using Portable Ultrasound and Multidomain "
Bibliographic record
Abstract
"This dataset contains multidomain clinical and functional measurements collected from 40 community-dwelling older women to investigate neuromuscular\u2013cognitive aging phenotypes. The dataset includes portable ultrasound\u2013derived quadriceps muscle thickness, hand-grip strength, bioimpedance-based adjusted skeletal muscle index (ASMI), Montreal Cognitive Assessment (MoCA) scores, anthropometric variables (age, height, weight, BMI), and lower-extremity function indicators (gait speed, chair-stand time, and SPPB total score). All measurements were obtained using standardized clinical protocols performed by trained examiners.The dataset was originally developed for an explainable unsupervised machine-learning study aimed at identifying latent phenotypes representing distinct combinations of muscle morphology, strength, body composition, and cognitive performance. These data support research in digital phenotyping, geriatric assessment, sarcopenia classification, physical function modeling, and multimodal clustering. The dataset is suitable for PCA, clustering, feature importance analysis, predictive modeling, and validation of digital biomarker frameworks.All data are fully anonymized and contain no personally identifiable information. The study procedures were approved by an Institutional Review Board, and written informed consent was obtained from all participants. This dataset provides a valuable benchmark for researchers developing interpretable machine-learning models, digital health tools, or multimodal assessment systems for aging populations"
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.011 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".