Bibliometric Analysis of Global Research on Colorectal Cancer Based on SCI-E
Bibliographic record
Abstract
Objective To assess the worldwide research status and the development trend of colorectal cancer. Methods Based on the papers of colorectal cancer indexed in SCI-E database from 2000 to 2019, the year of publication, countries, institutes, sources of journals, discipline fields, highly cited papers in ESI and research hotspots were analyzed by the bibliometric method. Results A total of 160183 papers on colorectal cancer were retrieved, and the number of papers increased year by year. The top 10 countries were USA, China, Japan, Germany, the United Kingdom, Italy, South Korea, France, Netherlands and Canada. Harvard University in the USA had a significant advantage on the research of colorectal cancer. The rank of the journals, institutes and highly cited papers were absolutely dominated by USA. Global research activities on the colorectal cancer displayed the characteristics of interdisciplinary development. The research hotspots mainly focus on the gut microbiota, genetic testing, targeted therapy, immunotherapy and so on. Conclusion It is necessary to strengthen the close combination of basic research and clinical application, and carry out colorectal cancer research in interdisciplinary collaboration with clinical problems.
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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 | 0.007 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.185 | 0.216 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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, 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".