Bibliographic record
Abstract
Dear Sir, Thank you for the opportunity to respond to the several comments made by Dr Girard regarding our meta-analysis of recombinant FSH (rFSH) versus urinary FSH (uFSH). We would like to start by reiterating that our search to identify all trials (whether published or not) that met our clearly specified inclusion criteria strategy was as comprehensive as possible. Consequently, we are surprised with Dr Girard's assertion that there was selective inclusion of some trials. Since the publication of our study, we have identified several more unpublished trials which are currently being evaluated for possible inclusion in an updated meta-analysis; we are still awaiting responses from the investigators of two studies regarding our questions about their study methods and data and have contacted N.V. Organon for their assistance because follitropin beta was used in both studies. We would be grateful to both Dr Girard and N.V. Organon for drawing to our attention any trials they believe we have missed so that the necessary evaluation of eligibility can be performed.
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.
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 | 0.005 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.015 | 0.015 |
| Insufficient payload (model declined to judge) | 0.019 | 0.014 |
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".