Technical factors, surgeon case volume and positive margin rates after breast conservation surgery for early-stage breast cancer.
Bibliographic record
Abstract
BACKGROUND: For patients with breast cancer, a negative surgical margin at first breast-conserving surgery (BCS) minimizes the need for reoperation and likely reduces postoperative anxiety. We assessed technical factors, surgeon and hospital case volume and margin status after BCS in early-stage breast cancer. METHODS: We performed a retrospective cohort study using a regional cancer centre database of patients who underwent BCS for breast cancer from 2000 to 2002. We considered the influence of patient, tumour and technical factors (e.g., size of specimen and preoperative diagnosis of cancer available) and surgeon and hospital case volume on margin status at first and final operation. We performed univariate and multivariate regression analyses. RESULTS: We reviewed 489 cases. There were no differences in patient or tumour characteristics among the low-, medium- and high-volume surgeon groups. High-volume surgeons were significantly more likely than other surgeons to operate with a confirmed preoperative diagnosis and to resect a larger volume of tissue. In our univariate analysis and at first operation, the rates of positive margins were 16.4%, 32.9% and 29.1% for high-, medium- and low-volume surgeons, respectively (p = 0.002). In the multivariate analysis, tumour factors (palpability, size, histology), presence of a confirmed preoperative diagnosis and size of resection specimen significantly predicted negative margins. However, when we controlled for these and other factors, high surgeon volume was not a predictor of negative margins at first surgery (odds ratio 1.8, 95% confidence interval 0.9-3.8, p = 0.09). Increased hospital volume was not associated with a lower rate of positive margins at first surgery. CONCLUSION: Various tumour and technical factors were associated with negative margins at first BCS, whereas surgeon and hospital volume status were not. Technical steps that are under the control of the operating surgeon are likely effective targets for quality initiatives in breast cancer surgery.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".