Predicting 3‐Year Survival in Older People with Intellectual Disabilities Using a Frailty Index
Bibliographic record
Abstract
OBJECTIVES: To analyze the relationship between frailty and survival in older people with intellectual disabilities (IDs). DESIGN: Population-based longitudinal observational study. SETTING: Three Dutch care provider services. PARTICIPANTS: Individuals with borderline to profound ID aged 50 and older (N=982). MEASUREMENTS: A frailty index (FI) including 51 health-related deficits was used to measure frailty. Mean follow-up was 3.3 years. The Cox proportional hazards model was used to evaluate the independent effect of frailty on survival. The discriminative ability of the FI was measured using a receiver operating characteristic (ROC) curve. RESULTS: Greater FI values were associated with greater risk of death, independent of sex, age, level of ID, and Down syndrome. There was a nonlinear increase in risk with increasing FI value. For example, mortality risk was 2.17 times as great (95% confidence interval (CI)=0.95-4.95) for vulnerable individuals (FI 0.20-0.29) and 19.5 (95% CI=9.13-41.8) times as great for moderately frail individuals (FI 0.40-0.49) as for relatively fit individuals (FI<0.20). The area under the ROC curve for 3-year survival was 0.78. CONCLUSION: Although the predictive validity of the FI should be further determined, it was strongly associated with 3-year mortality. Care providers working with people with ID should be able to recognize frail clients and act in an early stage to stop or prevent 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".