The State of Research and Development in Global Cancer Surgery
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to perform an analysis of global cancer surgery research and development trends over the last 10 years across 21 countries. BACKGROUND: Surgery is the main modality for cancer cure and control globally. Yet, in comparison to other areas such as cancer drugs, we know little about ongoing research activities to inform policymakers. METHODS: Two subfield filters, surgery research and oncology, were developed and applied to Web of Science. The intersection of these 2 filters identified papers in surgical oncology, and their bibliographic details were downloaded for analysis. This included matching of 5-year citation counts to the papers, impact factor, geographical analysis by country, translational collaboration, involvement in clinical trials, citation on clinical guidelines, and percentage of reviews. RESULT: Surgical oncology represents about 9% of all cancer research-low in comparison with surgery's contribution to cancer treatment. The US published the most, followed by Japan which had a high relative commitment to surgery within cancer research, followed by the large West European countries. Although Sweden's papers were relatively basic, it participated the most in clinical trials. Its papers were also the most cited on clinical guidelines, but contained relatively few reviews, where the UK, Greece, and Belgium scored best. Surgical oncology papers are generally not well cited compared with cancer research overall, but on this measure the Netherlands, the US, and Sweden scored best. International collaboration was measured relative to what might have been expected, on this indicator Canada, Switzerland, and the US were the best performers. CONCLUSIONS: Globally, low activity-low funding cycle needs to be addressed by new national and supranational policies to support surgical oncology research.
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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 | 0.046 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.025 | 0.077 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".