Prognostic profiles and the effectiveness of assisted conception: secondary analyses of individual patient data
Bibliographic record
Abstract
BACKGROUND: At present, it is unclear which treatment strategy is best for couples with unexplained or mild male subfertility. We hypothesized that the prognostic profile influences the effectiveness of assisted conception. We addressed this issue by analysing individual patient data (IPD) from randomized controlled trials (RCTs). METHODS: We performed an IPD analysis of published RCTs on treatment strategies for subfertile couples. Eligible studies were identified from Cochrane systematic reviews and we also searched Medline and EMBASE. The authors of RCTs that compared expectant management (EM), intracervical insemination (ICI), intrauterine insemination (IUI), all three with or without controlled ovarian stimulation (COS) and IVF in couples with unexplained or male subfertility, and had reported live birth or ongoing pregnancy as an outcome measure, were invited to share their data. For each individual patient the chance of natural conception was calculated with a validated prognostic model. We constructed prognosis-by-treatment curves and tested whether there was a significant interaction between treatment and prognosis. RESULTS: We acquired data from 8 RCTs, including 2550 couples. In three studies (n = 954) the more invasive treatment strategies tended to be less effective in couples with a high chance of natural conception but this difference did not reach statistical significance (P-value for interaction between prognosis and treatment outcome were 0.71, 0.31 and 0.19). In one study (n = 932 couples) the strategies with COS (ICI and IUI) led to higher pregnancy rates than unstimulated strategies (ICI 8% versus 15%, IUI 13% versus 22%), regardless of prognosis (P-value for interaction in all comparisons >0.5), but at the expense of a high twin rate in the COS strategies (ICI 6% versus 23% and IUI 3% versus 30%, respectively). In two studies (n = 373 couples), the more invasive treatment strategies tended to be more effective in couples with a good prognosis but this difference did not reach statistical significance (P-value for interaction: 0.38 and 0.68). In one study (n = 253 couples) the differential effect of prognosis on treatment effect was limited (P-value for interaction 0.52), perhaps because prognosis was incorporated in the inclusion criteria. The only study that compared EM with IVF included 38 couples, too small for a precise estimate. CONCLUSIONS: In this IPD analysis of couples with unexplained or male subfertility, we did not find a large differential effect of prognosis on the effectiveness of fertility treatment with IUI, COS or IVF.
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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 | 0.134 | 0.261 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.030 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".