Bibliographic record
Abstract
Reproductive diseases have a significant impact on human health, especially on women's health: endometriosis affects 10% of all reproductive-aged women but is often undiagnosed for many years, and preeclampsia claims over 70,000 maternal and 500,000 neonatal lives every year. Infertility rates are also rising. However, relatively few new treatments or diagnostics for reproductive diseases have emerged in recent decades. Here, based on analyses of PubMed, we report that the number of research articles published on non-reproductive organs is 4.5 times higher than the number published on reproductive organs. Moreover, for the two most-researched reproductive organs (breast and prostate), the focus is on non-reproductive diseases such as cancer. Further, analyses of grant databases maintained by the Canadian Institutes of Health Research and the National Institutes of Health in the United States show that the number of grants for research on non-reproductive organs is 6-7 times higher than the number for reproductive organs. Our results suggest that there are too few researchers working in the field of reproductive health and disease, and that funders, educators and the research community must take action to combat this longstanding disregard for reproductive science.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".