Outcomes of pregnancy-associated breast cancer: a Meta-analysis
Bibliographic record
Abstract
Objective To compare the case-fatality rate (CFR) and relapse rate (RR) between patients with pregnancy-associated breast cancer (PABC) and non-PABC and explore the possible factors contributing to such differences. Methods We searched the online databases including PubMed, CNKI, Wanfang, and VIP for case-control or cohort studies describing the outcomes of patients with breast cancer diagnosed during or within 1 year after pregnancy. The Newcastle-Ottawa Scale (NOS) was used for literature quality evaluation. The odds ratio (OR) and 95% confidence interval (95% CI) were calculated using RevMan5.3, and the publication bias of the retrieved articles was evaluated. Results We retrieved 23 articles that compared the CFR between patients with PABC (n=1 999) and non-PABC (n=13 271). The results of meta-analysis showed that the patients with PABC had a significantly higher CFR than those with non-PABC (OR=1.46, 95% CI: 1.31-1.63; P=0.40, I 2=5%). Meta-analysis of 5 articles that compared the RR between patients with PABC (n=188) and non-PABC (n=493) showed that the patients with PABC had a significantly higher RR than those with non-PABC (OR=2.10, 95%CI: 1.45-3.04; P=0.30, I2=18%). The funnel plots indicated no publication bias in all the articles included in this analysis. Conclusion The patients with PABC have a poorer prognosis than those with non-PABC.
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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 | 0.018 | 0.035 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.072 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".