Bibliographic record
Abstract
Background: Definitive diagnosis of a phyllodes tumour can only be done after excision of the lesion.However, malignant and borderline phyllodes require resection with a margin while benign phyllodes and fibroadenomas do not.Pre-operative prediction of the need for a margin will be advantageous.Methods: 31 lesions with a core biopsy suggestive of a phyllodes tumour were identified.Mammographic, ultrasound (US), and demographic data (age and source) were assessed while blinded to surgical outcomes.The features of lesions requiring a margin and those that did not were compared.Statistical analysis used Chi-square Fisher's exact test and ROC curves.Results: Of 31 assessed lesions, 13 required a margin and 18 did not.There were 6 screening-detected lesions, which were benign.Features found more frequently in those requiring a margin were poorly-defined margin on mammography [7/9 (78%) vs 4/13 (31%) p =0.04]; on ultrasound, irregular shape [8/13 (62%) vs 3/18 (17%) p= 0.01], microlobulations [7/13 (54%) vs 3/18 (17%) p = 0.028], mixed echogenicity [9/13 (69%) vs 1/18 (6%) p = 0.0002], echogenic clefts [6/13 (46%) vs 1/18 (6%) p = 0.007), BIRADS score > 3 [11/13 (85%) vs 9/18 (50%) p=0.047], distal enhancement [9/11 (82%) vs 6/18 (33%) p=0.01], ultrasound size and stiffness at shear-wave elastography, were also predictors: area under the curve (AUC) 0.76, p=0.003 and AUC 0.71, p=0.026 respectively. Conclusion(s):We have identified pre-operative features which can be used to guide surgical choice of margin when excising lesions with a core biopsy suggestive of a phyllodes tumour.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.334 | 0.258 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".