Association of progesterone receptor status with breast cancer prognosis: a meta-analysis
Bibliographic record
Abstract
BACKGROUND: Breast cancer (BC) prognosis is influenced by hormones, of which progesterone receptor (PR) status is controversial for BC prognosis, possibly related to clinical characteristics. OBJECTIVE: This study was to determine the impact of PR status on BC prognosis and explore the differences across patient subgroups. METHODS: PubMed, Cochrane Library, Embase, and Web of Science were searched for relevant studies until July 2024. NOS (Newcastle-Ottawa Scale) was leveraged for quality appraisal. Meta-analysis was performed using STATA15.1. RESULTS: Thirty-five studies were included, involving 89,164 patients. PR-negative status was associated with worse overall survival compared to PR-positive status (HR 1.70, 95%CI 1.42 to 2.04; p < 0.001). Similar results were unveiled for disease-free survival (HR 1.62, 95%CI 1.23 to 2.14, p < 0.001), breast-cancer-specific survival (HR 2.45, 95% CI 1.85 to 3.23, p < 0.001), and recurrence-free survival (HR 1.47, 95% CI 1.21 to 1.79, p < 0.001). Subgroup analyses unveiled that conclusions were influenced by region, estrogen receptor (ER) status, human epidermal growth factor receptor 2 (HER2) status, menopausal status, and metastatic status. CONCLUSION: PR loss is associated with worse outcomes in BC, which is influenced by clinical characteristics. Especially in patients with ER + HER2- tumors, PR status may serve as an additional predictive marker.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.051 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".