A multi‐institutional prediction model to estimate the risk of recurrence and mortality after mastectomy for <scp>T1‐2N1</scp> breast cancer
Bibliographic record
Abstract
BACKGROUND: Post-mastectomy radiation therapy (PMRT) in women with pathologic stage T1-2N1M0 breast cancer is controversial. METHODS: Data from five North American institutions including women undergoing mastectomy without neoadjuvant therapy with pT1-2N1M0 breast cancer treated from 2006 to 2015 were pooled for analysis. Competing-risks regression was performed to identify factors associated with locoregional recurrence (LRR), distant metastasis (DM), overall recurrence (OR), and breast cancer mortality (BCM). RESULTS: A total of 3532 patients were included for analysis with a median follow-up time among survivors of 6.8 years (interquartile range [IQR], 4.5-9.5 years). The 2154 (61%) patients who received PMRT had significantly more adverse risk factors than those patients not receiving PMRT: younger age, larger tumors, more positive lymph nodes, lymphovascular invasion, extracapsular extension, and positive margins (p < .05 for all). On competing risk regression analysis, receipt of PMRT was significantly associated with a decreased risk of LRR (hazard ratio [HR], 0.21; 95% confidence interval [CI], 0.14-0.31; p < .001) and OR (HR, 0.76; 95% CI, 0.62-0.94; p = .011). Model performance metrics for each end point showed good discrimination and calibration. An online prediction model to estimate predicted risks for each outcome based on individual patient and tumor characteristics was created from the model. CONCLUSIONS: In a large multi-institutional cohort of patients, PMRT for T1-2N1 breast cancer was associated with a significant reduction in locoregional and overall recurrence after accounting for known prognostic factors. An online calculator was developed to aid in personalized decision-making regarding PMRT in this population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".