Innovative estimation of survival using log-normal survival modelling on ACCENT database
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: The ACCENT database, with individual patient data for 20 898 patients from 18 colon cancer clinical trials, was used to support Food and Drug Administration (FDA) approval of 3-year disease-free survival as a surrogate for 5-year overall survival. We hypothesised substantive differences in survival estimation with log-normal modelling rather than standard Kaplan-Meier or Cox approaches. METHODS: Time to relapse, disease-free survival, and overall survival were estimated using Kaplan-Meier, Cox, and log-normal approaches for male subjects aged 60-65 years, with stage III colon cancer, treated with 5-fluorouracil-based chemotherapy regimens (with 5FU), or with surgery alone (without 5FU). RESULTS: Absolute differences between Cox and log-normal estimates with (without) 5FU varied by end point. The log-normal model had 5.8 (6.3)% higher estimated 3-year time to relapse than the Cox model; 4.8 (5.1)% higher 3-year disease-free survival; and 3.2 (2.2)% higher 5-year overall survival. Model checking indicated greater data support for the log-normal than the Cox model, with Cox and Kaplan-Meier estimates being more similar. All three model types indicate consistent evidence of treatment benefit on both 3-year disease-free survival and 5-year overall survival; patients allocated to 5FU had 5.0-6.7% higher 3-year disease-free survival and 5.3-6.8% higher 5-year overall survival. CONCLUSION: Substantive absolute differences between estimates of 3-year disease-free survival and 5-year overall survival with log-normal and Cox models were large enough to be clinically relevant, and warrant further consideration.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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