The knowledge domain and emerging trends in the infertility field: A 67-year retrospective study
Bibliographic record
Abstract
Infertility is a significant problem influencing many couples. Our purpose was to assess the field of infertility in Obstetrics and Gynecology from 1955 to 2022 reviewing 3575 documents found in the Web of Science database. Most articles were in the areas of Reproductive Biology, Fertility, Endometriosis & Hysterectomy, and Chromosome Disorders. We found publication has increased dramatically since 1989. Agarwal, Thomas, and Sharma; United States, England, and Canada; Fertility and Sterility, Human Reproduction, and AJOG were the most-cited authors, countries, and journals, respectively. We discovered five substantive clusters: male infertility factors, female infertility factors, causes and treatment of infertility, the consequence of infertility, and assisted reproductive techniques. Using bibliometric review (Co-citation analysis) six research areas were found: semen analysis and sperm morphology, regional differences in the psychological effects of infertility, unexplained infertility, endometriosis, diagnosis and treatment of infertility, and polycystic ovary syndrome. Despite advances in understanding infertility, further research is needed.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".