Nutrition Risk Factors for Survival in the Elderly Living in Canadian Long‐Term Care Facilities
Bibliographic record
Abstract
OBJECTIVES: To determine the role of nutritional parameters in influencing the risk of mortality in institutionalized elderly. DESIGN: A prospective cohort study in which subjects had several nutritional parameters measured at baseline and were followed for 19 months. Time to death and mortality were recorded starting immediately after enrollment. SETTING: Fourteen long-term care facilities (LTCFs). PARTICIPANTS: Four hundred eight elderly long-term care residents aged 60 and older who resided in the facility for more than 6 weeks. MEASUREMENTS: At baseline, knee height, weight, mid-arm circumference (MAC), skin-fold thickness, and fat-free mass using bioelectric impedance analysis were measured. Covariates included demographic factors, length of stay in the facility, functional status, and medical diagnoses. Cox proportional hazards regression analysis was used to identify independent predictors of mortality. Results are reported as mean+/-standard error of the mean (SEM). RESULTS: Overall, mortality rate was 28.4%. Univariate predictors included male sex, body mass index, MAC, and triceps skin fold. In multivariate analysis, male sex (hazard ratio (HR)=1.7, 95% confidence interval (CI)=1.2-2.7, P=.0096) and MAC less than 26 cm were significantly associated with increased risk of mortality (HR=4.8, 95% CI: 2.8-8.3, P<.0001). CONCLUSION: Among this elderly population living in LTCFs, MAC is the best nutritional predictor of mortality.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".