Ovulation induction drug and ovarian cancer: an updated systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To explore the association between ovulation induction drugs and ovarian cancer. DESIGN: Systematic review and meta-analysis. SETTING: Not applicable. PATIENT(S): Women without ovarian cancer who ever or never underwent ovarian induction. INTERVENTION(S): An extensive electronic search of the following databases was performed: PubMed, EMBASE, MEDLINE, Google Scholar, Cochrane Library and CNKI, from inception until January 2022. A total of 34 studies fulfilled our inclusion criteria and were included in the final meta-analysis. The odds ratio (OR) and random-effects model were used to estimate the pooled effects. The Newcastle-Ottawa Scale was used to assess the quality of included studies. Funnel plots and Egger tests were used to assess publication bias. MAIN OUTCOMES: New diagnosed borderline ovarian tumor (BOT) and invasive ovarian cancer (IOC) between ovulation induction (OI) group and control (CT) group considering fertility outcome, OI cycles and specific OI drugs. RESULTS: Primarily, there was no significant difference in the incidence of IOC and BOT between the OI and CT groups. Secondly, OI treatment did not increase the risk of IOC and BOT in the multiparous women, nor did it increase the risk of IOC in the nulliparous women. However, the risk of BOT appeared to be higher in nulliparous women treated with OI treatment. Thirdly, among women exposed to OI, the risk of IOC and BOT was higher in nulliparous women than in multiparous women. Fourthly, the risk of IOC did not increase with increasing OI cycles. Lastly, exposure to specific OI drugs also did not contribute to the risk of IOC and BOT. CONCLUSION: Overall, OI treatment did not increase the risk of IOC and BOT in most women, regardless of OI drug type and OI cycle. However, nulliparous women treated with OI showed a higher risk of ovarian cancer, necessitating their rigorous monitoring and ongoing follow-up.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".