Bibliographic record
Abstract
We thank Lin et al 1 for their interest and for their thoughtful comments on our article. They question the validity of our analysis, indicating a potential bias because of the exclusion of patients who discontinued hormonal therapy as a result of disease progression and death from causes other than prostate cancer. We agree with their point, and we did address the limitations of our hypothesis-generating analysis, including this issue, in our article. o overcome the specific limitation mentioned by Lin et al and to address the issue of bias, we performed a landmark analysis looking only at those patients who survived beyond 5 years in all three groups. The results of this analysis have been published in a previous correspondence to Journal of Clinical Oncology. Briefly, of the total of 189 patients who received less than 5 years of hormonal therapy in the original analysis, 145 (77%) were alive for at least 5 years. There were no significant changes in the distribution of pretreatment characteristics for the smaller, landmark subset of patients compared with the original 189-patient cohort. The 5-year landmark analysis continues to show a trend for an overall survival difference favoring more than 5 years hormonal duration (11-year survival, 64% v 50%), although this difference is no longer statistically significant (P .2), perhaps because of the smaller sample. There are, however, still statistically significant differences among the three hormonal therapy duration groups with respect to disease-free survival (P .0009), local failure (P .02), and distant metastases (P .002), favoring the longer hormonal therapy duration.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.031 | 0.047 |
| Insufficient payload (model declined to judge) | 0.008 | 0.010 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".