Inverse relationship between body mass index and mortality in older nursing home residents: a meta‐analysis of 19,538 elderly subjects
Bibliographic record
Abstract
Body mass index (BMI) and mortality in old adults from the general population have been related in a U-shaped or J-shaped curve. However, limited information is available for elderly nursing home populations, particularly about specific cause of death. A systematic PubMed/EMBASE/CINAHL/SCOPUS search until 31 May 2014 without language restrictions was conducted. As no published study reported mortality in standard BMI groups (<18.5, 18.5-24.9, 25-29.9, ≥30 kg/m(2)), the most adjusted hazard ratios (HRs) according to a pre-defined list of covariates were obtained from authors and pooled by random-effect model across each BMI category. Out of 342 hits, 20 studies including 19,538 older nursing home residents with 5,223 deaths during a median of 2 years of follow-up were meta-analysed. Compared with normal weight, all-cause mortality HRs were 1.41 (95% CI = 1.26-1.58) for underweight, 0.85 (95% CI = 0.73-0.99) for overweight and 0.74 (95% CI = 0.57-0.96) for obesity. Underweight was a risk factor for higher mortality caused by infections (HR = 1.65 [95% CI = 1.13-2.40]). RR results corroborated primary HR results, with additionally lower infection-related mortality in overweight and obese than in normal-weight individuals. Like in the general population, underweight is a risk factor for mortality in old nursing home residents. However, uniquely, not only overweight but also obesity is protective, which has relevant nutritional goal implications in this population/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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.032 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".