Bibliometric profile of the global scientific research on methanol poisoning (1902–2012)
Bibliographic record
Abstract
Methanol poisoning is on the rise and has been associated with high morbidity and mortality; it has resulted in growing research in the field of toxicology. The aim of this study was to reveal underlying patterns in scientific outputs related to methanol poisoning at the global level by evaluating different bibliometric indices.We searched for publications that contained specific words regarding methanol poisoning in Scopus database.A total of 912 articles, with 8,317 citations and with an average of 9.1 citations per document, were retrieved on methanol poisoning, and the bulk of the articles were published from the USA (20.9%), followed by Spain (4.4%), Canada (4.3%), India (3.1%), and France (3.0%). The articles were published belonging to 57 countries. No data related to methanol poisoning were published from 155 (73.1%) out of 212 countries. Twenty-one documents (2.3%) were published in Clinical Toxicology, whereas 18 (2.0%) were published in The Lancet.Scientific production related to methanol poisoning is increasing. articles have been published in a wide range of journals with a variety of subject areas, most notably clinical toxicology; and the country with the greatest production was the USA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.024 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".