A bibliometric analysis of publications on trauma in critical care medicine during 1980-2018: A holistic view
Bibliographic record
Abstract
BACKGROUND: There is not a holistic bibliometric study evaluating the publications in the literature even though trauma is a paramount subject in the field of critical care. This study aimed to investigate the important articles and journals receiving the most citations and publishing the most articles, revealing international cooperation and uncovering trend topics in this subject as a consequence of analyzing articles on trauma in the field of critical care published between 1980 and 2018 using bibliometric analysis methods. METHODS: Publication scan in this study was performed using Web of Science (WoS) database. Literature review was limited to only publications indexed in the field of Critical Care. "Trauma" was used as the keyword to reach relevant publications. Linear regression analysis was performed to predict the number of articles foreseen to be published in the upcoming years in the subject of trauma. RESULTS: As a result of the literature review, a total of 10851 publications were found. Six thousand four hundred and eighty-nine (59.8%) of these publications were under the category of article. First three countries with the most publications were respectively as the United States of America (4096) (63.1%), Canada (401) and Germany (380). Turkey ranked 21 with 41 publications. The article titled "Evaluating trauma care - the triss method" published in 1987 had received the most citations. The journal with the most publications and citations was "Journal of Trauma Injury Infection and Critical Care". CONCLUSION: This study will be a useful guide to all scientists and clinicians conducting research on trauma in critical care.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.126 | 0.142 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".