Trends in Global Assisted Reproductive Technologies Research: a Scientometrics study
Bibliographic record
Abstract
INTRODUCTION: This study illustrated the global contribution to assisted reproductive technologies (ARTs) research in MEDLINE database from 1998 to 2014. METHODS: In March 2015, the MEDLINE database was searched for research publications indexed under 'reproductive techniques, assisted' (including the following MeSH headings: in vitro fertilization [IVF]; intracytoplasmic sperm injections; cryopreservation; and ovulation induction), with the following expressions in the fields of title or abstract: intrauterine insemination; sperm donation; embryo/egg donation and surrogate mothers. The number of publications in MEDLINE database was recorded for each individual year, 1998-2014, and for each country. The following countries were arbitrarily selected for data retrieval: United States, United Kingdom, France, Germany, Canada, Italy, Japan (G7 countries), Brazil, Russia, India, China (BRIC countries), Egypt, Turkey, Israel and Iran. RESULTS: The absolute number of publications for each country from 1998 to 2014 ranged from 75 to 16453, with a median of 2024. The top five countries were the US (16453 publications), the UK (5427 publications), Japan (4805), China (4660) and France (3795). ART (20277), cryopreservation (11623) and IVF (11209) were the most researched areas. CONCLUSION: Global research on ARTs were geographically distributed and highly concentrated among the world's richest countries. Cryopreservation and IVF were the most productive research domains among ARTs.
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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 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.071 | 0.155 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".