Assessing diagnostic accuracy improvement for survival or competing‐risk censored outcomes
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
Abstract Diagnostic accuracy studies have progressed in the past decade to consider survival outcomes beyond the traditional dichotomous outcome. Another recent advance is the appearance of novel measures for diagnostic accuracy improvement by adding new markers. In this paper we attempt to integrate these two evolving areas and contribute a discussion on assessing diagnostic accuracy improvement for censored survival outcomes. More importantly, we consider competing‐risk censoring in addition to independent censoring, and provide inferential procedures. Particularly, we consider fitting regression models based on cumulative incidence functions for the primary event, and propose parallel estimators for the adapted accuracy improvement measures based on inverse probability weighting and bivariate cumulative incidence function estimation. Both estimators perform very well in simulations and in an application to a breast cancer study. The Canadian Journal of Statistics 42: 109–125; 2014 © 2014 Statistical Society of Canada
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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 | 0.153 |
| 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 it