DEFINING ELIGIBILITY CRITERIA FOR FUNDING POLICIES AROUND <i>IN VITRO</i> FERTILIZATION
Bibliographic record
Abstract
OBJECTIVES: This review aims to assess the state of the science around the potential impact of certain patient characteristics on the safety and effectiveness of in vitro fertilization (IVF). METHODS: Following Cochrane Collaboration guidelines and the PRISMA statement, a comprehensive systematic review of reviews and recent primary studies examining the impact of paternal age and maternal age, smoking, and body mass index (BMI) on the safety and effectiveness of IVF was performed. Papers, published between January 2007 and June 2014, were independently reviewed and critically appraised by two researchers using published quality assessment tools for reviews and primary studies. Due to heterogeneity across papers (different study designs and patient selection criteria), a qualitative analysis of extracted information was performed. RESULTS: Seventeen papers (ten systematic reviews and seven primary studies) were included. They comprised evidence from retrospective observational studies in which maternal age, BMI, and smoking status were explored as part of secondary analyses of larger studies. The majority of papers found that the likelihood of achieving a pregnancy was lower among women who were >40 years, had a BMI ≥ 25 and smoked. Advanced maternal age and BMI were also associated with higher rates of preterm birth and low birth weight. CONCLUSIONS: Based on available evidence, it may be appropriate to consider "maternal age" and "morbid obesity" in public funding policies that aim to maximize the effectiveness of IVF. However, given inconsistencies in the effect of smoking across different pregnancy-related outcomes, support for incorporating it into funding conditions appears weak.
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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 | 0.248 | 0.497 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.014 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".