Decision Editor: Merril Silverstein, PhD Examining Late-Life Functional Limitation Trajectories and Their Associations With Underlying Onset, Recovery, and Mortality
Bibliographic record
Abstract
Objectives. Identify common trajectories of physical functional limitation and mortality among Taiwanese 50 and older as they age, link these to underlying transitions in onset, recovery, and mortality, and assess associations between trajectories and a set of risk factors. Method. Longitudinal data from 4 waves of the Survey of Health and Living Status of the Middle-Aged and Elderly in Taiwan, collected between 1996 and 2007, are analyzed using a summary measure indicating difficulty with one or more of 7 physical functions. A group-based trajectory model identifies common trajectories. Shorter term transition probabilities that underlie multiperiod trajectories are examined. Multinomial regression associates risk factors with trajectory group membership. Results. Best fitting the data is a model with 3 groups characterized as early, mid, and late onset of limitation. Roughly half the population follows the trajectory of mid onset and a quarter each of the other 2. Females and those least educated are most likely to belong to the early-onset group. Discussion. The analysis advances understanding of late-life functioning by focusing on the heterogeneity of func-tional limitation experience, appropriately accounting for the relation between functional limitation and mortality and
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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 | 0.009 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.063 | 0.028 |
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".