Does Adjuvant Radiation Therapy Benefit Women with Small Mammography-Detected Breast Cancers?
Bibliographic record
Abstract
Background: Women with small nonpalpable breast tumours have an excellent prognosis. The benefit of radiotherapy in this group of low-risk women is unknown. Methods: A cohort of 1595 women with stages i–iii invasive breast cancer treated with breast-conserving surgery were followed for local recurrence. Using t-tests, baseline demographic data and tumour characteristics were compared for the women who had palpable (n = 1023) and mammography-detected (n = 572) breast cancers. The 15-year actuarial risk of local recurrence was estimated using a Kaplan–Meier method, stratified for adjuvant radiation therapy (yes or no), tumour palpability (palpable or not), and tumour size (≤1 cm or >1 cm). Hazard ratios (hrs) and 95% confidence intervals (95% cis) were calculated using a multivariate Cox regression model. Results were considered statistically significant if 2-tailed p values were less than 0.05. Results: Among women with a nonpalpable tumour, the 15-year actuarial rates of local recurrence were, respectively, 13.9% and 18.3% for those treated and not treated with adjuvant radiation therapy (hr: 0.65; 95%ci: 0.40 to 1.06; p = 0.08). Among women with small nonpalpable breast cancers (≤1.0 cm), the rates were 14.6% and 13.4% respectively (p = 0.67). The absolute reduction in 15-year local recurrence was 11.0% for women with palpable tumours. Conclusions: Our results suggest that women with small (<1 cm) screen-detected nonpalpable breast cancers likely derive little benefit from adjuvant radiotherapy; however, an adequately powered randomized trial would be required to make definitive conclusions.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".