Gynecologic oncology top-cited articles: an international analysis
Bibliographic record
Abstract
The aim of this paper was to study the top-cited per year (CPY) original articles published in the leading subspecialty journals in gynecologic oncology and in the leading general obstetrics and gynecology journals. We used the Web of Science and iCite databases to mine the original articles and review articles in the field of gynecologic oncology in the following journals: Gynecologic Oncology, The International Journal of Gynecological Cancer, The American Journal of Obstetrics and Gynecology and the Obstetrics & Gynecology. Top CPY articles from the four journals were analyzed and compared in a two-time point analysis. A total of 23,252 original articles and reviews were identified. The 100 Top-CPY articles were published from 1983 to 2021. Seventy (70%) in Gynecologic Oncology journal, 20 (20%) in The International Journal of Gynecological Cancer, eight (8%) in Obstetrics & Gynecology and two (2%) in The American Journal of Obstetrics and Gynecology. The most common study methodology was observational studies (20%), followed by guidelines/consensus papers (19%). The most common study topic was ovarian cancer (41%). North America originating authors composed 62% of the top CPY publications, followed by Europe (21%). The most common country of authorship was the United States (52%) followed by Canada (10%). CPY were similar in the publications before vs. after 2014 (P=.19). Study designs, study topics and continent of authorship were similar in both periods. The proportion of multi-center studies was higher after 2014 (66.6% vs. 28.8%, P=0.002) and the proportion of open access publications was higher after 2014 (66.6% vs. 15.4%, P<.001). Funded studies were more common after 2014 (75.0% vs. 53.8%, P=0.028). Ovarian cancer is the top CPY area of research in gynecologic oncology. This field is leaded by authors from the United States with multi-center studies proportion increasing in recent years. It is important to promote further high-quality research in other countries to disseminate knowledge and equality.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.076 | 0.090 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".