"Digital Phenotyping of Neuromuscular\u2013Cognitive Aging Using Portable Ultrasound and Multidomain "
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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"
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it