Evolution of military medicine literature: a scientometric study of global publications on military medicine between 1978 and 2017
Bibliographic record
Abstract
OBJECTIVES: Scientometrics is a popular statistical discipline providing data relevant to publication patterns and trends in a certain academic field. There are no scientometric analyses of publications produced in military medicine literature. The present study aims to perform a holistic analysis of military medicine literature. METHODS: . All indexed documents between 1978 and 2017 were included. Countries, authors, institutions, citations and keywords relevant to the military medicine literature were comprehensively analysed. An infomap revealing global productivity and infographics of scientometric networks were generated. RESULTS: A total of 48 240 published items were found, 82.29% of which were original articles. USA, covering 56.66% of all literature dominated the military medicine field followed by the UK, China, Canada and Israel. We found that 18 of 20 most productive institutions in the world were from USA and the US Department of Defense was the most contributing institution in the literature with 9664 documents. The most used keywords over a 40-year period were 'military', 'veterans', 'posttraumatic stress disorder' and 'military personnel'. A scientometric network of keywords showed a complicated 'starburst pattern'. CONCLUSION: All most contributing countries except Turkey, China and Israel were developed nations. Only one institution (Tel Aviv University) from developing countries was noted in the list of 20 most productive institutions. The researchers from developing and the least developed countries should be encouraged and supported to carry out novel studies on military medicine.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.091 | 0.111 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".