The value of multicenter collaboration in Gynecologic Oncology research
Bibliographic record
Abstract
INTRODUCTION: Canadian gynecological oncology (GYNONC) is constantly evolving. We aim to study the patterns in Canadian GYNONC research using a systematic search approach and bibliometric analysis. EVIDENCE ACQUISITION: We used Web of Science to identify all relevant publications in the field of GYNONC by Canadian. We analyzed bibliometric data obtained from the iCite database. Publications were evaluated for specific characteristics including the province of all co-authors. We compared bibliometric metrics among provinces. EVIDENCE SYNTHESIS: Overall, 1511 publications, published in 138 different journals during 1973-2022 were analyzed. Of those, 23.5% (N.=355) were of interprovincial origin. Interprovincial publications were constantly increasing, now reaching 34.1%. Publications of interprovincial setting had higher RCR, CPY, FCR and NIH percentile scores when compared to any single province (P=0.009, P>0.001, P<0.001, and P<0.001, respectively). The proportion of publications in high impact factor journals were higher in the interprovincial setting: 35 (9.9%) vs. 48 (4.2%), P<0.001. Excluding the interprovincial publications there were 1156 publications. Half of the publications were authored by authors from Ontario (N.=587, 50.6%), 278 (24.1%) by authors from Quebec, and 161 (14.0%) by authors from British Columbia. The mean FCR was higher in British Columbia as compared to Ontario, Quebec and Manitoba (6.0±2.1 vs. 5.3±2.1, 5.3±1.5, and 4.1±3.0 respectively; P=0.006, P=0.034, and 0.037, respectively). Only Ontario, Quebec, British Columbia and Alberta had publications in high impact factor journals, with similar rate (P=0.806). CONCLUSIONS: Interprovincial publications have the highest citation metrics in all domains. This underscores the importance of collaboration for the purpose of impactful research.
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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 | Metaresearch Domain: Methods · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
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 | 0.133 | 0.330 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.034 | 0.069 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 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".