A systematic review and meta-analysis of pregnancy-associated breast cancer incidence rate
Bibliographic record
Abstract
BACKGROUND: Pregnancy-Associated Breast Cancer (PABC) is a special type of breast cancer that either occurs during pregnancy or one year postpartum. The aim of this systematic review and meta-analysis is to investigate the global incidence of PABC. METHODS: In this meta-analysis, to find related studies, three international databases including PubMed (Medline), Scopus and Web of Science (Clarivate analytics) were explored. An additional search was also carried out using Google Scholar in December 2023 looking for any new relevant article, and the list of references for all new supposedly relevant papers were manually searched for and investigated as well. The required data were extracted from retrieved studies and the quality of the studies was evaluated using the Newcastle-Ottawa scale checklist. Heterogeneity among studies was assessed by I-square statistic and chi-square test and due to presence of a significant heterogeneity among studies, a random-effects model was used to pool the data. RESULTS: Twenty-two studies were included in this meta-analysis. Among 51,944,490 number of female individuals included in the study, a total number of 7,267 cases of PABC were identified. Based on these results, the global incidence of PABC was estimated 19.2 cases per 100,000 pregnancies (95%CI: 16.1-22.2, I-square = 98.9%). The results of cumulative analysis showed that the incidence rate of PABC has risen over decades, as it increased from 13.3 cases (in 1969) to 19.2 cases (in 2022) per 100,000 pregnancies. The lowest incidence rate belonged to the American continent with 14.4 (95%CI: 9.8-19) cases per 100,000 pregnancies. CONCLUSIONS: The results obtained from this study demonstrates that the global incidence of PABC amounts to 19.2 cases per 100,000 pregnancies and it has been increasing slowly during the last few decades as time went by. The incidence rate in developing countries seem to be higher than in the developed countries. However, more studies are required in order to reach a better conclusion on this issue.
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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 | 0.022 | 0.052 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.063 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".