Global cocaine intoxication research trends during 1975–2015: a bibliometric analysis of Web of Science publications
Bibliographic record
Abstract
BACKGROUND: Cocaine is subject to recreational abuse as a stimulant and psychoactive agent, which poses a major worldwide health problem. The aim of the present study was to perform a bibliometric analysis of publication related to cocaine intoxication an insight of the research trends at a global level to enable recommendations for future research strategies in this field. METHODS: Publications about cocaine intoxication were retrieved from the Web of Science (WoS) Core Collection database on December 28, 2016, and analysed regarding the following bibliometric indicators: research trends, document types, languages, countries/territories with their h-index, collaboration patterns, journals with their impact factors (IF), and institutions. RESULTS: In total, 2,902 scientific publications from 1975 to 2015 were retrieved from the WoS database. The annual number of publications related to cocaine toxicity increased slightly after 1990 and reached a peak of 148 in 1992, with an average of 103 publications per year. The USA outranked other countries/territories with 2,089 publications, of which 1,927 arose exclusively from the USA and 162 involved international collaborations. The h-index for all publications related to cocaine was 212, and the h-index for all publications related to cocaine intoxication was 99. Moreover, the USA had the highest h-index of 95, followed by Spain with h-index of 24, and Canada with h-index of 24. The main research topics were consistently reproductive toxicity, clinical management of acute cocaine exposure, laboratory methods for detection of exposure to cocaine, cocaine metabolism, and cocaine toxicity in animals. CONCLUSIONS: This is the first bibliometric approach to examining research related to cocaine toxicity and shows that research activity has become more global and extensive since 1990. The USA remains the leading country regarding published literature, the highest h-index, and greatest role in international collaborations.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.107 | 0.148 |
| 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) | 0.002 | 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".