Bibliographic record
Abstract
results, 2 they might play a role in explaining the discrepancies between the Olivotto et al series and ours.However, other published series using ER and PR immunohistochemistry, have a figure for the ERϪ/PRϩ population in the range of 2% to 4%. 3,4 We further analyzed the benefit derived from adjuvant tamoxifen in our series of patients according to the hormone receptor subgroups.It is remarkable that, when compared with the ERϪ/PRϪ subgroup, the ERϪ/PRϩ subset of patients had, at least during the first years of treatment, a benefit from tamoxifen.This is shown in Figure 1.Threeyear disease-free survival figures were ERϩ/PRϩ, 90%; ERϪ/PRϩ, 82%; ERϩ/PRϪ, 77%; and ERϪ/PRϪ, 76%.Six-year disease-free survival was ERϩ/PRϩ, 85%; ERϪ/ PRϩ, 74%; ERϩ/PRϪ, 72%; and ERϪ/PRϪ, 72%.In conclusion, it would seem inappropriate to stop progesterone receptor testing, since the PR test helps in identifying a non-negligible subset of breast cancer patients who will most likely benefit from adjuvant hormonal therapy.Whether PR testing should be reserved for ERϪ tumors or performed in all cases might be an interesting issue for further exploration.
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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 | 0.004 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.021 | 0.026 |
| Insufficient payload (model declined to judge) | 0.025 | 0.018 |
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".