Anthropometric Characteristics of Hospitalised Elderly Women: A Case-Control Study
Bibliographic record
Abstract
This study assessed the anthropometric status of 451 hospitalised female patients aged 70 or over, at their admission to hospital, in reference to 77 healthy women of the same age. The most frequent diseases were circulatory diseases (40.8%), mental disorders (29.9%), respiratory diseases (12.4%), endocrine and metabolic diseases (11.5%), osteomuscular diseases (8.4%), and traumatisms (6.9%). The differences were significantly high for mid-arm circumference (MAC), triceps skinfold thickness (TSF), weight, weight/height, and body mass index (BMI). The patients with cancers, blood diseases, mental disorders, respiratory disease, digestive diseases, or traumatisms had the lowest values. All the indicators correlated in a similarly negative way with age. The decreased TSF was more pronounced among subjects with respiratory diseases. Measurement of anthropometric indicators, TSF in particular, should be part of preventive measures aimed at reducing malnutrition and its consequences in a hospital setting.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".