Validation of a Machine Learning Model for Predicting Postmastectomy Radiotherapy Recommendation Following Immediate Breast Reconstruction
Bibliographic record
Abstract
Background: Postmastectomy radiotherapy (PMRT) in the context of immediate implant-based breast reconstruction (IIBBR) is associated with long-term morbidity. The likelihood of PMRT may influence the type and timing of breast reconstruction chosen in the preoperative setting. This study aimed to validate a machine learning (ML) model for predicting the probability of PMRT recommendations in IIBBR patients, in accordance with the transparent reporting of studies on prediction models for individual prognosis or diagnosis guidelines. Methods: The study cohort comprised 224 breast cancer patients who underwent mastectomy with IIBBR from January 2021 to December 2022. Data were collected on 12 patient characteristics identified as predictive in our ML model. Preoperative characteristics were recorded from clinical history, physical examination, diagnostic imaging, and pathology reports. Results: Of the 224 patients, 37% (n = 84) were recommended PMRT. Our ML model demonstrated high predictive performance, with an area under the receiver operating characteristic curve score of 0.80 (0.74-0.86). The variables most predictive of PMRT recommendation included the size of suspicious lymph nodes, the presence of carcinoma in axillary lymph node biopsies, tumor size, and the use of ultrasound as the initial diagnostic modality. Conclusions: An ML model for predicting PMRT recommendations following IIBBR was validated. This prediction model may be helpful in the preoperative clinical setting to inform the discussion of reconstructive options.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".