Obstetric and Perinatal Outcomes in Female Cancer Survivors: Protocol of a Pooled Analysis of Population-based Cohort Studies
Bibliographic record
Abstract
Abstract Introduction Approximately 80% of children, adolescents and young adults (CAYA) cancer patients are expected to fully recover and survive for five years or more. The reproductive health is a particular area of their concern. Evidence demonstrates that previous therapeutic treatments for cancer and comorbidities may have harmful effects on female fertility and delivery outcomes, which will significantly affect patient quality of life. However, these reports are heterogeneous. Therefore, the purpose of this study is to provide the up-to-date evidence of associations between cancer and obstetric and perinatal outcomes. Methods and analysis This meta-analysis will be carried out and reported with adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and the Cochrane Handbook. We will search online databases including PubMed, Cochrane Library, and Embase until March, 2022 to identify all relevant cohort studies examining the relationship between cancer and subsequent obstetric and perinatal outcomes. Data extraction from eligible studies will be conducted independently by two unblinded investigators using pre-tested standardized data extraction forms and crosschecked by a senior investigator. Discrepancies will be resolved via discussion until consensus is reached. Methodological quality assessment for each study will be conducted using the Newcastle–Ottawa scale (NOS) tool. We will apply the DerSimonian-Laird random-effects model to calculate the pooled estimates. Further sources of heterogeneity will be explored by performing subgroup analysis based on multiple study characteristics. Potential publication bias will be assessed by inspection of a funnel plot, Begg’s and Egger’s regression tests of funnel plot asymmetry. Ethics and dissemination Ethical approval will not be required as all data used for this pooled analysis will be obtained from published cohort studies. The results of this study will be disseminated in a peer-reviewed journal and conference presentation.
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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 | 0.155 | 0.176 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.019 | 0.041 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.026 | 0.004 |
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".