Reply: International estimates on infertility prevalence and treatment seeking: potential need and demand for medical care
Bibliographic record
Abstract
Sir, We welcome the in-depth attention Dyer gave our article on the international prevalence of infertility. Dyer questioned the validity of our conclusion on the basis of misinterpretation of original data, omission of relevant studies and extrapolation of data that was not representative across regions. In our study, we identified 25 population surveys evaluating infertility prevalence and reported the current median 12-month infertility prevalence to be 9% for more (3.5–16.7%) and less developed (9.2–9.3%) countries excluding studies using a 24 month period. Although this exclusion was made clear in the Results we did not re-iterate it in the Discussion or the Abstract and we agree with Dyer that this omission might have led to confusion about the pool of studies that were used to generate our median prevalence. It is of interest that where 12 and 24 month estimates were available from the same study, the difference was not large (8.5 and 7%, respectively) (Royal Commission, 1993).
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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 | 0.008 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.022 | 0.036 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".