Surgical margins for borderline and malignant phyllodes tumours
Bibliographic record
Abstract
Background Phyllodes tumours represent less than 1% of all UK breast neoplasms. Histological features allow classification into benign, borderline or malignant, which has a significant impact on prognosis and recurrence. Currently, there is no consensus for the optimal surgical excision margin. This systematic review aims to provide a comparative summary of outcomes (local recurrence, metastasis and survival) for borderline and malignant phyllodes tumours resected with either ≥1cm or <1cm margins. Methods MEDLINE and Embase were systematically searched (1990 to July 2019), in line with PRISMA guidelines. Study quality was assessed using the Newcastle–Ottawa scale. Results Ten retrospective studies were included (Newcastle–Ottawa scale mean score: 5.6, range: 8–4). Nine reported local recurrence rates, four reported distant metastasis and four reported survival. Meta-analysis pooling demonstrated no statistically significant difference between <1cm and ≥1cm margins in terms of local recurrence rates (relative risk [RR] 1.43, 95% confidence interval [95% CI] 0.70 – 2.93; p=0.33, n=456), distant metastasis (RR 1.93, 95% CI 0.35 – 10.63; p=0.45, n=72) or mortality (RR 1.93, 95% CI 0.42 – 8.77; p=0.40, n=58) for borderline and malignant tumours. Additionally, two studies demonstrated no significant difference in local recurrence for borderline tumours excised with <0.1cm margins compared to ≥1cm. Conclusion Current evidence suggests that margins <1cm may provide adequate tumour excision. This could enable breast conservation in patients with smaller breast-to-tumour volume ratios, with improved cosmetic outcomes and patient satisfaction. A prospective, multi-institutional trial would be appropriate to further elucidate the safety of smaller margins.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".