Nutritional risk and time to death; predictive validity of SCREEN (Seniors in the Community Risk Evaluation for Eating and Nutrition).
Bibliographic record
Abstract
BACKGROUND: Undernutrition in community-living seniors is common and has the potential to adversely influence health outcomes. Nutritional risk screening tools can help identify seniors at risk, but few have predicted health outcomes. METHODS: Seniors were recruited from 23 community service providers. The 8-item abbreviated version SCREEN (Seniors in the Community Risk Evaluation for Eating and Nutrition) was used to identify nutritional risk in 367 seniors; demographics, health, activities of daily living, and psychosocial variables were included in a baseline assessment. The seniors were followed-up by telephone for 18 months to determine the occurrence of health outcomes, including death. Cox regression was used to identify predictors of survival time. RESULTS: During the 18-month follow-up there were 27 deaths (approximately 7%). Using the abbreviated tool, nutritional risk was common (42.2%). This low rate of death limited the modeling to only a few key covariates, which were based on bivariate analyses. Nutritional risk was significantly associated with time to death. Gender was also associated with time to death, with men more likely to die sooner than women. Increasing age was also significantly associated with shorter survival times. CONCLUSIONS: Nutritional risk as measured by SCREEN was predictive of time to death. This simple tool may be useful for future epidemiological research on health outcomes of seniors. Further work should confirm these results, as the low event rate influenced the modeling strategy.
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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 | 0.002 | 0.014 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".