Trends analysis of cancer topic of Cochrane systematic reviews: a bibliometric analysis
Bibliographic record
Abstract
Abstract Purpose: To analyze the scientific outputs of the cancer topic of Cochrane systematic reviews (Cancer-CSR) in order to have a comprehensive understanding and lay the foundation for the following research. Patients and methods: Cochrane Database of Systematic Review and Web of Science Core Collection were retrieved limited from Jan. 1, 2009 to Dec. 12, 2018. CiteSpace IV and Excel 2018 were applied to analyze and visualize the literature information. Results: Ultimately, 607 Cancer-CSR were retrieved, 32 countries, 179 institutions and 260 authors involved. The number of publications in Cancer-CSR has been increasing over the past decades (25(2009)-77(2018)). UK, USA, Canada, Australia, and Germany worked closely with other countries, especially the UK (n=361) has taken the lead in this field. The top 10 contributive institutions, which were almost came from developed countries, collaborated closely with other institutions. Cochrane Database Syst Rev, C Hdb Sys and J Clin Oncol were the top three journals with the highest co-citation. The top three co-cited references were the two different version of Cochrane handbook for systematic reviews and the guidelines of Review Manager. The biggest cluster of keywords “cytoreductive surgery (CRS)” and the latest clusters “visual inspection” and “non-steroidal anti-inflammatory drug” were the most promising hotspots. Conclusions: Cancer-CSR has been increasing. Most of the reviews were came from the developed countries as well as the institutes in these countries. The knowledge base of was the methodology studies of systematic review, epidemiological data of cancer, and the reporting guideline of systematic reviews. The adjuvant therapy combined CRS, the screening of skin cancer and the management of cancer-related pain were the hotspots. Reasons that influence the author's preference for Cancer-CSR also deserve further investigation.
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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 | 0.045 | 0.287 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.014 |
| Bibliometrics | 0.215 | 0.264 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".