Trends in Global Assisted Reproductive Technologies Research: a Scientometrics study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.020 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it