Life table (survival) analysis to generate cumulative pregnancy rates in assisted reproduction: are we overestimating our success rates?
Bibliographic record
Abstract
The variability in the numbers of treatment cycles couples may undertake with assisted reproductive technology (ART) and the length of time they may have to wait between successive cycles of treatment make the evaluation of treatment efficacy and prognosis complicated. The cumulative pregnancy rate using the life table method of analysis is being used more frequently to estimate the effectiveness of treatment. Although this approach is valid in some areas of infertility research, its use in ART is not appropriate, because the factors necessary for the analysis (particularly the scale for measuring the passage of time and lack of informative censoring) are not satisfied. Consequently, an overestimation of the effect of treatment is produced that may lead to biased decision making. Although there is no easy solution to this problem, several options for summarizing the outcome data are offered: pregnancy rate per cycle, time-limited analysis using proportions, conservative cycle-based cumulative pregnancy rate and real-time-based cumulative pregnancy rate. In this manner, more realistic information can be generated to counsel patients, evaluate the efficacy of treatments, compare rates among centres and guide the formulation of policies for infertility management and resource allocation.
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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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".