Do surgical oncologists achieve lower rates of local‐regional recurrence in node positive breast cancer treated with mastectomy alone?
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Adjuvant radiotherapy for node positive breast cancer postmastectomy has been recommended by two previously published randomized controlled trials (RCT). The local-regional recurrence rates in the control arms, however, were considered by some critics to be excessive (> 25% at 10 years). Inadequate surgery, as evidenced by the low number of axillary nodes reported, may have resulted in the high local-regional recurrence rates, allowing for the benefits seen with radiotherapy. Fellowship trained surgical oncologists might provide "better quality" surgery, resulting in lower recurrence rates and thus making adjuvant radiotherapy unnecessary. Our objective was to establish the local-regional control rate postmastectomy in node positive breast cancer patients operated on by surgical oncologists, and to determine if treatment recommendations from previous RCTs are generalizable. METHODS: Node positive stage IIb and IIIa breast cancer patients treated with mastectomy at the Medical College of Virginia Hospitals by surgical oncologists, without adjuvant radiotherapy, and entered into adjuvant chemotherapy trials between 1978 and 1993 were identified retrospectively. Pathology and follow-up records were reviewed. RESULTS: One hundred and thirty-seven patients were identified. A median of 18 axillary nodes was reported with a median of 4 positive nodes. The locoregional recurrence at 10-years was 27% (95% confidence interval, 19-35%). CONCLUSION: Despite some evidence of "better quality" surgery, there was no clinically significant difference in the local-regional recurrence rate in this case series compared to controls in two previous RCTs. Recommendations for postmastectomy radiotherapy should be considered for node positive breast cancers, even if operated upon by surgical oncologists.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.004 | 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".