Executive Function and Survival in the Context of Chronic Illness
Bibliographic record
Abstract
BACKGROUND: Individual differences in executive function (EF) have been shown to predict risk factors for chronic illness. It is not currently known whether EFs also predict survival time following a diagnosis of a chronic illness. PURPOSE: The objective of this investigation was to examine the association between individual differences in EF and survival time among individuals suffering from one or more chronic illness. METHODS: A sample of 162 community-dwelling older adults who suffered from a chronic illness at baseline underwent thorough medical and neurological examinations to ensure freedom from actual or probable dementia. Participants completed cognitive testing and were subsequently followed for 10 years; survival was assessed as survival time over the follow-up interval. RESULTS: Findings indicated that individual differences in EF predicted survival time, and this association held when adjustments were made for demographic variables (age, sex), education, and body mass index. CONCLUSION: Individual differences in EF may be important determinants of survival in the context of chronic illness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".