Inference for covariate‐adjusted time‐dependent prognostic accuracy measures
Why this work is in the frame
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Bibliographic record
Abstract
Evaluating the prognostic performance of candidate markers for future disease onset or progression is one of the major goals in medical research. A marker's prognostic performance refers to how well it separates patients at the high or low risk of a future disease state. Often the discriminative performance of a marker is affected by the patient characteristics (covariates). Time-dependent receiver operating characteristic (ROC) curves that ignore the informativeness of the covariates will lead to biased estimates of the accuracy parameters. We propose a time-dependent ROC curve that accounts for the informativeness of the covariates in the case of censored data. We propose inverse probability weighted (IPW) estimators for estimating the proposed accuracy parameters. We investigate the performance of the IPW estimators through simulation studies and real-life data analysis.
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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 | 0.002 | 0.147 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it