How Wide Should Margins Be for Phyllodes Tumors of the Breast?
Bibliographic record
Abstract
The surgical management of phyllodes tumors (PTs) is still controversial. Some studies have suggested surgical margins ≥1 cm, but recent studies suggested that negative margins could be appropriate regardless of their width. To evaluate recurrence rates of PTs following surgery according to margins. Retrospective study of women who attended a tertiary breast cancer reference center between 1998 and 2010: 142 patients with a PT diagnosis, either at minimally invasive breast biopsy or at surgery, were identified. Clinical, pathologic and follow-up characteristics were assessed. Among 140 patients who underwent surgery, 64.3% of biopsies accurately predicted the final PT diagnosis at surgery. Forty-two (42/87, 48.3%) PTs had positive margins. Twenty-one (21/42, 50.0%) patients had a surgical revision of margins. Only one (1/42, 2.4%) had margins greater or equal to 1 cm. After a median follow-up of 1.29 years in benign PTs, 4.99 years in borderline PTs, and 5.42 years in malignant PTs, there were five local recurrences, three in originally benign PTs and two in borderline PTs. All were managed with surgery. Four had initial margins ≤1 mm. One patient with borderline PT had a local recurrence and later progressed to regional recurrence and metastasis. Free surgical margins are necessary to treat PT, and margins of at least 1 mm might be sufficient to prevent recurrence. Core needle biopsy might not be the best diagnostic tool for PTs.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".