Improving Prediction of Risk of Admission to Long-Term Care or Mortality Among Home Care Users With IDD
Bibliographic record
Abstract
BACKGROUND: Frailty is an established predictor of admission into long-term care (LTC) and mortality in the elderly population. Assessment of frailty among adults with intellectual and developmental disabilities (IDD) using a generic frailty marker may not be as predictive, as some lifelong disabilities associated with IDD may be interpreted as a sign of frailty. This study set out to determine if adding the Home Care-Intellectual and Developmental Disabilities Frailty Index (HC-IDD Frailty Index), developed for use in home care users with IDD, to a basic list of predictors (age, sex, rural status, and the Johns Hopkins Frailty Marker) increases the ability to predict admission to long-term care or death within one year. METHODS: A retrospective cohort study was conducted using Residential Assessment Instrument for Home Care (RAI-HC) data for adult home care users with IDD who had a home care assessment between January 1, 2010 and December 31, 2013 (N = 6,169). RESULTS: value < .0001). CONCLUSIONS: We recommend the use of the HC-IDD Frailty Index in care planning and in further research related to the effectiveness of interventions to reduce or delay adverse age-related outcomes among adults with IDD.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".