Local recurrence of phyllodes tumors after surgery with wide compared to narrow margins: study protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Phyllodes tumors are rare fibroepithelial breast lesions graded as benign, borderline, or malignant. Surgical resection with clear margins is the primary method of treatment. Consensus on margin width could prevent unnecessary large primary resections or reoperations, yet the optimal margins for different tumor grades remain unclear. This systematic review and meta-analysis will evaluate the effect of wide versus narrow margins on local recurrence rates after surgery for phyllodes tumors of the breast. In addition, the re-excision rates, adjuvant treatment and adverse events will be assessed. METHODS: An information specialist will assist in searching MEDLINE, EMBASE, Web of Science, Google Scholar, and Cochrane Library databases, as well as gray literature to identify randomized controlled trials, observational studies, and case series. Relevant abstracts from professional society meetings and web-based registries of clinical trials will also be included via hand-search and by forward-tracking papers and by searching the reference lists of the obtained articles. Studies included will compare patients, without age limitation, who were surgically treated for a histopathologically confirmed phyllodes tumor of the breast. Studies reporting both local recurrence rate and surgical excision margins will be included. No language restriction will be applied. Two reviewers will independently screen the titles and abstracts of the studies identified during the search using pre-defined inclusion criteria and data extraction from the full texts of selected studies will be performed. The quality of included studies will be assessed by two independent reviewers using the Cochrane Risk of Bias 2.0 tool for randomized trials, the Newcastle-Ottawa Scale for observational studies and the Joanna Briggs Institute Checklist for case series. A meta-analysis on pooled local recurrence rates will be conducted, stratified by different phyllodes tumor grades and surgical margins. DISCUSSION: This systematic review will provide a synthesis of current evidence on the optimal surgical margins of phyllodes tumors and its effect on local recurrence rates. These findings aim to provide clinicians with guidelines and to establish a strong research base for future studies in this field. TRIAL REGISTRATION: PROSPERO CRD420250640098.
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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 | 0.047 | 0.065 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.025 | 0.030 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.052 | 0.005 |
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".