Evidence-Based Medicine for Treatment: An In Vitro Fertilization Trial
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it