Robotic surgery in obstetrics and gynecology: a bibliometric study
Why this work is in the frame
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Bibliographic record
Abstract
We aimed to identify the trends and patterns of robotic surgery research in obstetrics and gynecology since its implementation. We used data from Clarivate's Web of Science platform to identify all articles published on robotic surgery in obstetrics and gynecology. A total of 838 publications were included in the analysis. Of these, 485 (57.9%) were from North America and 281 (26.0%) from Europe. 788 (94.0%) articles originated in high-income countries and none from low-income countries. The number of publications per year reached a peak of 69 articles in 2014. The subject of 344 (41.1%) of articles was gynecologic oncology, followed by benign gynecology (n = 176, 21.0%) and urogynecology (n = 156, 18.6%). Articles discussing gynecologic oncology had lower representation in low- and middle-income countries (LMIC) (32.0% vs. 41.6%, p < 0.001) compared with high income countries. After 2015 there has been a higher representation of publications from Asia (19.7% vs. 7.7%) and from LMIC (8.4% vs. 2.6%), compared to the preceding years. In a multivariable regression analysis, journal's impact factor [aOR 95% CI 1.30 (1.16-1.41)], gynecologic oncology subject [aOR 95% CI 1.73 (1.06-2.81)] and randomized controlled trials [aOR 95% CI 3.67 (1.47-9.16)] were associated with higher number of citations per year. In conclusion, robotic surgery research in obstetrics & gynecology is dominated by research in gynecologic oncology and reached a peak nearly a decade ago. The disparity in the quantity and quality of robotic research between high income countries and LMIC raises concerns regarding the access of the latter to high quality healthcare resources such as robotic surgery.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.055 | 0.043 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it