Investigating the epidemiology of methanol poisoning outbreaks: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: We aim to identify relevant studies from 2000 to 2020 regarding methanol poisoning outbreaks and map the existing literature with a focus on the epidemiology and global burden of disease. INTRODUCTION: Methanol poisoning occurs in individuals or as an outbreak. Illicit productions are responsible for most methanol poisoning outbreaks; however, there are some occupational, suicidal, and homicidal incidences as well. In methanol poisoning outbreaks, medical facilities get overwhelmed quickly. The current WHO fact sheet dates back to 2014 and there have been no updates since. Based on our preliminary search, it seems that the course of methanol outbreaks has changed. INCLUSION CRITERIA: The study will include peer-reviewed articles and gray papers that focus on the epidemiology of methanol poisoning outbreaks. This review will consider all methanol poisoning outbreak victims without any limitation in geographical, social, cultural, or gender-based demographics. METHODS: A three-step search strategy will be used. First, an initial search will be done in MEDLINE and Scopus to identify key terms. Those key terms will then be searched across included databases (MEDLINE, Scopus, Embase, and Web of Science) and sources for gray literature. In a third step, references and Google Scholar will be searched manually. Two reviewers will screen the titles and abstracts, then full texts for identifying inclusion criteria and data extraction. Disagreements will be resolved by a senior author. Extracted data will be tabulated and mapped. Quantitative data will be reported using descriptive numerical summary analysis.
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
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 | 0.110 | 0.085 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.014 | 0.013 |
| Bibliometrics | 0.026 | 0.017 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.053 | 0.012 |
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".