Validity of self-reported height and weight estimates in cognitively-intact and impaired elderly individuals.
Bibliographic record
Abstract
OBJECTIVE: A high prevalence of undernutrition has been observed in the elderly, particularly in cognitively impaired or demented individuals. Self-reported height and weight were tested as simple and non-invasive methods to efficiently screen individuals at risk. DESIGN: Cross-sectional study. PARTICIPANTS: A subset of subjects (n=465) participating in the longitudinal follow-up phase of the Canadian Study of Health and Aging (CSHA) and comprising cognitively intact and impaired individuals as well as demented subjects. MEASUREMENTS: Self-reported values of height and weight were compared to direct standard measurements using Pearson's correlation coefficients and linear regressions by cognitive status. Estimation bias was determined using paired Student t-tests. Sensitivity and specificity of body mass index (BMI) derived from self-reported data were calculated. RESULTS: Self-reported and measured weights were highly correlated (r>.90) in all three categories of cognitive status. A tendency to underestimate their weight was observed in overweight women. Correlations of recalled to measured height were excellent in normal (r=.91) and good in cognitively impaired (r=.86) and demented (r=.85) subjects. A systematic overestimation of recalled height was observed, particularly among individuals of short stature. Self-reported BMI showed excellent sensitivity (>93%) in detecting underweight individuals in all three categories. CONCLUSION: Self-reported height and weight data can be obtained in normal and cognitively-impaired elderly persons as well as in mild or moderate cases of dementia and can be used as a valid tool to screen for risk of undernutrition.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".