Effects of impairment in activities of daily living on predicting mortality following hip fracture surgery in studies using administrative healthcare databases
Bibliographic record
Abstract
BACKGROUND: Impairment in activities of daily living (ADL) is an important predictor of outcomes although many administrative databases lack information on ADL function. We evaluated the impact of ADL function on predicting postoperative mortality among older adults with hip fractures in Ontario, Canada. METHODS: Sociodemographic and medical correlates of ADL impairment were first identified in a population of older adults with hip fractures who had ADL information available prior to hip fracture. A logistic regression model was developed to predict 360-day postoperative mortality and the predictive ability of this model were compared when ADL impairment was included or omitted from the model. RESULTS: The study sample (N = 1,329) had a mean age of 85.2 years, were 72.8% female and the majority resided in long-term care (78.5%). Overall, 36.4% of individuals died within 360 days of surgery. After controlling for age, sex, medical comorbidity and medical conditions correlated with ADL impairment, addition of ADL measures improved the logistic regression model for predicting 360 day mortality (AIC = 1706.9 vs. 1695.0; c -statistic = 0.65 vs 0.67; difference in - 2 log likelihood ratios: χ(2) = 16.9, p = 0.002). CONCLUSIONS: Direct measures of ADL impairment provides additional prognostic information on mortality for older adults with hip fractures even after controlling for medical comorbidity. Observational studies using administrative databases without measures of ADLs may be potentially prone to confounding and bias and case-mix adjustment for hip fracture outcomes should include ADL measures where these are available.
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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 | 0.021 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".