Evidence-Based Medicine for Treatment: An In Vitro Fertilization Trial
Bibliographic record
Abstract
Evidence-based evaluation of treatment is a pivotal component of an effective and satisfying clinical practice. When the best evidence has been identified, it can be efficiently assessed on three levels: Are the methods valid? Is the effect sufficiently large to be meaningful to patients? Are the patients, intervention(s), and outcomes studied applicable to our own patients? These criteria were applied to a multicenter trial that evaluated whether intracytoplasmic sperm injection (ICSI) was superior to in vitro fertilization (IVF) among infertile couples with no known male factor who were on a waiting list for IVF. The study was a well-designed randomized controlled trial that effectively concealed the randomization list and took reasonable steps to exclude bias. The results seemed important because the number needed to treat (13) was relatively low and significant, but the primary outcome (implantation rate) was not clinically meaningful. The trial results would have been relevant to most infertile couples with no known male factor if it had been powered to evaluate a difference in a more relevant clinical outcome, such as live birth. Thus, it has not been shown definitively that ICSI is inferior to IVF among couples with no known male factor, and clinical demand for ICSI may continue to rise.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".