Addition of time‐dependent covariates to a survival model significantly improved predictions for daily risk of hospital death
Bibliographic record
Abstract
RATIONAL, AIMS AND OBJECTIVES: The study aims to determine the extent to which the addition of post-admission information via time-dependent covariates improved the ability of a survival model to predict the daily risk of hospital death. METHOD: Using administrative and laboratory data from adult inpatient hospitalizations at our institution between 1 April 2004 and 31 March 2009, we fit both a time-dependent and a time-fixed Cox model for hospital mortality on a randomly chosen 66% of hospitalizations. We compared the predictive performance of these models on the remaining hospitalizations. RESULTS: All comparative measures clearly indicated that the addition of time-dependent covariates improved model discrimination and prominently improved model calibration. The time-dependent model had a significantly higher concordance probability (0.879 versus 0.811) and predicted significantly closer to the number of observed deaths within all risk deciles. Over the first 32 admission days, the integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were consistently above zero (average IDI of +0.0200 and average NRI of 62.7% over the first 32 days). CONCLUSIONS: The addition of time-dependent covariates significantly improved the ability of a survival model to predict a patient's daily risk of hospital death. Researchers should consider adding time-dependent covariates when seeking to improve the performance of survival models.
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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 | 0.014 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".