The crossover design for studies of infertility employing in-vitro fertilization: A methodological survey
Bibliographic record
Abstract
BACKGROUND: Infertility has become increasingly common worldwide. There is a need for the infertility literature to evaluate new interventions with IVF. The crossover design presents many methodological advantages for IVF trials. In addition to providing a within-person comparison of outcomes, it offers participants the opportunity to potentially benefit from more than one available treatment. However, infertility studies present a unique challenge in terms of bias: successful participants do not cross over to the second treatment group. OBJECTIVES: The main objective of our study was to survey the methodological features of crossover trials for infertility with in-vitro fertilization (IVF) based interventions. A secondary focus was reporting key results. STUDY DESIGN & SETTING: We conducted a methodological survey by systematically searching Medline and Embase databases. The capture-recapture technique was used to estimate the number of relevant studies that were not retrieved by our search strategy. We employed the Cochrane risk of bias tool to assess methodological rigour. Crossover-specific methods features were summarized. Treatment effects for pregnancy outcomes across studies are also presented. RESULTS: 15 studies met inclusion criteria. Most studies were deemed to have high or unclear risks of bias, usually because of incomplete reporting of outcome data and assessment procedures. 13 studies did not employ crossover-specific methods to analyze outcome data by period, which may bias treatment effect estimates. Four studies reported pregnancy outcome data with sample sizes from both treatment periods. Of these four studies, three reported that the control intervention was favoured. CONCLUSIONS: The main limitation of our survey was the small sample size of studies. Future reviews should be larger and seek to encompass a broader range of the infertility literature. Despite the issues identified in the included trials, consideration should still be given to using the crossover design in future infertility research. Employing crossover-specific analysis methods, such as accounting for participant non-completion, along with strict adherence to CONSORT reporting guidelines, may significantly reduce the risk of bias in individual studies.
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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 | 0.435 | 0.493 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".