Global research productivity of <i>N</i> -acetylcysteine use in paracetamol overdose
Bibliographic record
Abstract
PURPOSE: The main objective of this study was to examine the publication pattern of N-acetylcysteine (NAC) research output for paracetamol overdose at the global level. METHODS: Data were searched for documents that contained specific words regarding NAC and paracetamol as keywords in the title and/or abstract and/or keywords. Scientific output was evaluated based on a methodology developed and used in other bibliometric studies. Research productivity was adjusted to the national population and nominal gross domestic product per capita. RESULTS: The criteria were met by 367 publications from 33 countries. The highest number of articles associated with the use of NAC in paracetamol overdose was from the United States of America (USA; 39.78%), followed by the United Kingdom (UK; 11.99%). After adjusting for economy and population power, USA (2.822), Iran (1.784) and UK (1.125) had the highest research productivity. The total number of citations at the time of data analysis (14 March 2014) was 8785 with an average of 23.9 citations per document and a median (interquartile range) of 6 (1-22). The h-index of the retrieved documents was 48. The highest h-index was 32 for USA, followed by 20 for UK. Furthermore, the highest number of collaborations with international authors for each country was held by USA with 11 countries, followed by Canada with 7 countries. CONCLUSION: The amount of NAC-based research activity was low in some countries, and more effort is needed to bridge this gap and to promote better evaluation of NAC use worldwide. Our findings demonstrate that NAC use for paracetamol overdose remains a hot issue in scientific research and may have a larger audience compared with other toxicological aspects. Editors and authors in the field of toxicology might usefully promote the submission of work on NAC in future to improve their journal's impact.
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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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.034 | 0.060 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| 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".