Trajectories of nutritional risk: The Manitoba follow-up study
Bibliographic record
Abstract
OBJECTIVES: To identify patterns of nutritional risk among older men over a four-year period and to project their survival rates over the next two and a half years. DESIGN: A prospective longitudinal study. SETTING: Canada. PARTICIPANTS: Three hundred and thirty-six male survivors of the Manitoba Follow-up Study (MFUS) cohort with an average age of 90 years in 2011. MEASUREMENTS: Four years of nutritional risk SCREEN II scores (five waves) from the male survivors of the MFUS cohort. The semi-parametric group-based trajectory approach and survival analysis were used to investigate the trajectories of nutritional risk. RESULTS: Of the participants, 30% lived alone. Five distinct developmental trajectory groups for nutritional risk score were identified. Significant statistical differences were found among the five trajectory groups for SF-36 mental health (p=.02), SF-36 physical health (p=<.001), perception of aging successfully (p=.04) and living alone (p=<.001). Among the five groups, the most pairwise differences were found in appetite, intake of meat and alternatives, and vegetables and fruit, weight change, skipping meals and eating with others. Men in the poorest nutritional risk trajectory group were two times more likely to die within a 2 1/2 year period compared to men in the best nutritional risk trajectory group (hazard rate = 2.33, p=.07). CONCLUSION: Distinct nutritional risk trajectories were found for older men over a four year period. Poor nutritional risk trajectories are associated with higher risk of mortality for very old men over a short period of time. Timely nutritional assessments by health professionals are needed to identify older men at nutritional risk. Subsequent nutrition education and follow-up may be important in preventing further decline.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".