Investigating the appropriateness of different concordance measures in a time‐to‐event setting
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: Prediction models that assess a patient's risk of an event are used to inform treatment options and confirm screening tests. The concordance (c) statistic is one measure to validate the accuracy of these models, but has many extensions when applied to censored data. The purpose was to determine which c-statistic is most accurate at different rates of censoring. METHODS: A simulation study was conducted for n = 750, and censoring rates of 20%, 50%, and 80%. The mean of three different concordance definitions were compared as well as the mean of three different c-statistics, including one, parametric c-statistic for exponentially distributed data, developed by the authors. The SE was also calculated but was of secondary interest. RESULTS: The c-statistic developed by the authors yielded the a mean closest to the gold standard concordance measure when censoring is present in data, even when the exponentially distributed parametric assumptions do not hold. Similar results were found for SE. CONCLUSIONS: The c-statistic developed by the authors appears to be the most robust to censored data. Thus, it is recommended to use this c-statistic to validate prediction models applied to censored data. This will improve the reliability and comparability across future time-to-event studies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it