Bibliographic record
Abstract
Dear Sir, We wish to respond to the comments made by Dr Koninckx regarding our study. We would like to begin by thanking him for acknowledging the methodological soundness and meticulousness of our meta-analysis and the clarity of the inferences we have drawn from the results. We are intrigued by Dr Koninckx's assertion that the significantly higher clinical pregnancy rate observed in the meta-analysis of data from randomized trials comparing recombinant FSH with urinary FSH may have been the result of factors other than the type of FSH that was used. The purpose of randomization is to generate control and experimental groups that are likely to be similar with respect to known and unknown covariates. The larger the sample size and the more secure the randomization process, the higher the likelihood that the study groups will be similar. Such a study design also ensures that each subject will have an equal chance of being assigned to either the experimental or the control group. Consequently, any differences observed in outcome can be attributed to the effect of the experimental intervention. In our meta-analysis, only randomized trials were included so that the possibility of bias, that is inherent in non-randomized studies, would be avoided. Therefore, the significantly higher clinical pregnancy rate observed in the experimental group can be attributed to the use of recombinant FSH.
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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 | 0.004 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.023 | 0.017 |
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".