Profile-specific survival estimates: Making reports of clinical trials more patient-relevant
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
BACKGROUND: When considering treatment options, a physician needs to know the prognosis corresponding to the risk profile of the patient seeking treatment. Reports of clinical trials generally address treatment-specific survival probabilities only in the aggregate, i.e., for the typical patient, and often express the difference in survival as a hazard ratio. Such summaries do not provide treatment-specific survival probabilities (and thus the absolute difference in these probabilities) for patient profiles that are not near the typical of those in the trial. Despite the fact that Cox intended his hazard regression method to be used to produce such profile-specific survival estimates, and even showed how to calculate them, authors are either unaware that this is possible, or else choose not to report them. PURPOSE: To illustrate how treatment- and profile-specific survival estimates are obtained from the Cox method, and can be displayed in a compact form. METHODS: We derive treatment- and profile-specific survival probabilities from the estimated survival function for the ;reference' profile. Data from the Systolic Hypertension in the Elderly Program study serve as an illustration. RESULTS: Two different formats, tabular and nomogram-based, allow the entire set of estimated treatment- and profile-specific survival probabilities to be reported. LIMITATIONS: Estimates are limited to the profiles within the covariate-space spanned by the trial, and depend on the correctness of the model. CONCLUSION: Treatment- and profile-specific survival estimates are practice-relevant, almost never reported, estimable from the Cox model, and easy to report in a compact form.
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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 | 0.138 | 0.718 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it