Predictors of post-mastectomy radiation (PMRT) in node-negative breast cancer patients.
Bibliographic record
Abstract
e12608 Background: Post-mastectomy radiation (PMRT) reduces the risk of locoregional failure for women with an elevated risk of recurrence from breast cancer. Therefore, PMRT is often indicated for women with node positive breast cancer including those with only 1-3 nodes involved. The need for PMRT in node negative breast cancer patients is less established. The objective of our study was to review the predictors of PMRT in women with node-negative breast cancer and evaluate the overall recurrence rates. Methods: A retrospective chart review was completed. Women with node-negative breast cancer who underwent mastectomy and sentinel lymph node biopsy at a regional breast cancer center between January 1st,2011 and December 31st, 2017 were included. Patient and tumor characteristics, treatment details and recurrence data were recorded. The primary outcome was recommendation of PMRT. Univariate analysis was completed and then a multivariable logistic regression was completed to determine independent predictors for PMRT. Results: Overall, 235 women with node-negative breast cancer underwent mastectomy and sentinel lymph node biopsy during the study period. Forty-three (18.3%) patients were recommended to undergo PMRT, with 39 of the 43 patients completing the recommended treatment. PMRT was offered more often to younger women (p<0.001), women with multifocal/centric disease (p=0.002), large tumors (p<0.001), high grade tumors (p < 0.001), lymphovascular positive tumors (p=0.04) and estrogen-negative disease (p =0.017). On multivariable analysis, the odds of radiation recommendation were highest for patients with high grade disease (OR 5.81, 95%CI: 2.08 – 16.20) followed by multifocal/centric disease (OR 3.12, 95%CI: 1.26 – 7.70). There were no differences in overall recurrence between patients who underwent PMRT versus those who did not have PMRT (p = 0.31). Conclusions: A moderate percentage of node negative patients are offered PMRT. Independent predictors for recommendation of PMRT in node negative patients are: decreasing age, increasing tumor size, multifocal/centric disease and higher grade disease. Surgeons can use this information to counsel patients regarding the possible need for PMRT, especially in the setting of planned immediate reconstruction.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".