Robotic Surgery in Gynecologic Oncology—A Bibliometric Study
Bibliographic record
Abstract
STUDY OBJECTIVE: To characterize robotic surgery publications in gynecologic oncology, and to identify factors associated with high citation metrics. DESIGN: A cross-sectional study SETTING: Original articles on robotic surgery in gynecologic oncology. PATIENTS: No patients involved. INTERVENTIONS: Robotic surgeries in gynecologic oncology. MEASUREMENTS AND MAIN RESULTS: We performed PubMed Medical Subject Headings search for original articles on robotic surgery in gynecologic oncology. We analyzed citation scores and income level of country of publication, as well as factors associated with high citation metrics. Overall, 566 studies during 2005 to 2023 were included. Of those 292, 51.6% were from North America, and 182 32.2% from Europe. The leading tumor site studied was endometrial cancer (57.4%). The majority (87.6%) of studies were retrospective and 13 (2.3%) were randomized controlled trials. Most studies (94.2%) originated in high-income countries. Articles from middle-income countries had lower citations per year as compared to high-income countries (median 1.6 vs 2.5, p =.002) and were published in lower-impact factor journals (median 2.6 vs 4.3, p < .001) when compared with high-income countries. Cervical cancer studies had higher representation in middle-income countries than in high-income countries (48.5% vs 18.4%, p < .001). In a multivariable regression analysis, journal's impact factor [aOR 95% CI 1.26 (1.12-1.40)], cervical cancer topic [aOR 95% CI 3.0 (1.58-5.91)], and North American publications [aOR 95% CI 2.07 (1.08-3.97)] were independently associated with higher number of citations per year. CONCLUSION: The majority of robotic surgery research in gynecologic oncology is retrospective and from high-income countries. Middle-income countries are not as frequently cited and are predominantly in lower-impact factor journals.
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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 | 0.011 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.072 | 0.099 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".