Ovarian cancer: density equalizing mapping of the global research architecture
Bibliographic record
Abstract
BACKGROUND: Despite its impact on female health worldwide, no efforts have been made to depict the global architecture of ovarian cancer research and to understand the trends in the related literature. Hence, it was the objective of this study to assess the global scientific performance chronologically, geographically and in regards to economic benchmarks using bibliometric tools and density equalizing map projections. METHODS: The NewQIS platform was employed to identify all ovarian cancer related articles published in the Web of Science since 1900. The items were analyzed regarding quantitative aspects (e.g. publication date, country of origin) and parameters describing the recognition of the work by the scientific community (e.g. citation rates). RESULTS: 23,378 articles on ovarian cancer were analyzed. The USA had the highest activity of ovarian cancer research with a total of n = 9312 ovarian cancer-specific publications, followed by the UK (n = 1900), China (n = 1813), Germany (n = 1717) and Japan (n = 1673). Ovarian cancer-specific country h-index also showed a leading position of the USA with an h-index (HI) of 207, followed by the UK (HI = 122), Canada (HI = 99), Italy (HI = 97), Germany (HI = 84), and Japan (HI = 81). In the socio-economic analysis, the USA were ranked first with an average of 175.6 ovarian cancer-related publications per GDP per capita in 1000 US-$, followed by Italy with an index level of 46.85, the UK with 45.48, and Japan with 43.3. Overall, the USA and Western European nations, China and Japan constituted the scientific power players publishing the majority of highly cited ovarian cancer-related articles and dominated international collaborative efforts. African, Asian and South American countries played almost no visible role in the scientific community. CONCLUSIONS: The quantity and scientific recognition of publications related to ovarian cancer are continuously increasing. The research endeavors in the field are concentrated in high-income countries with no involvement of lower-resource nations. Hence, worldwide collaborative efforts with the aim to exchange epidemiologic data, resources and knowledge have to be strengthened in the future to successfully alleviate the global burden related to ovarian cancer.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".