Bibliometric analysis of literature regarding ostomy research based on the Web of Science database
Bibliographic record
Abstract
Abstract Objective To analyze the literature status and research hotspots of Science Citation Index (SCI)-related ostomy in the world and to provide references for scientific research and clinical work in the stoma care field. Methods Based on the Web of Science core database and its own analysis function, HistCite analysis software and Excel were used to study the published research about ostomy patients. Results A total of 1,262 articles were published between 1910 and 2016 with the authors from 48 countries and regions, 1,347 research institutions, published in 321 journals, with 4,048 first authors and coauthors; globally, there was a trend of slow growth in the number of authors every year. The study in the USA was absolutely in the lead position, and Canada and Turkey were more active. China's circulation volume was the 15th in the world. The periodical that published most often was the Journal of Wound Ostomy and Continence Nursing The most interdisciplinary surgical studies were surgery and nursing, where these should be considered important. The most prolific author in the field was "Grant", and the highest cited article was entitled as "Living with a stoma: a review of the literature". Conclusions The related research of global stoma is constantly developing. The research hotspot is nursing before and after stoma surgery. China and the USA are leading countries in research. They should follow the recent trend to improve the depth and breadth of the research in the field.
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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 | 0.008 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.274 | 0.244 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".