The Incidence and Risk Factors of Carbon Monoxide Poisoning in the Middle East and North Africa: Systematic Review
Bibliographic record
Abstract
Background: Carbon monoxide poisoning (COP) represents a significant burden and potential cause of death. However, there are only a few studies about COP in Saudi Arabia despite its noticeable impact on the community. Objective: This systematic review aimed to estimate the prevalence of COP at the level of Middle Eastern and North African region based on WHO distribution, with a particular interest to extrapolate the case fatality rate (CFR) and complications rates among COP victims. Methods: By reviewing the databases (Cochrane Library, Pubmed, EMBASE, and CINAHL) in a strategic approach, the relevant studies were retrieved and reviewed independently. Studies were included based on a predefined set of criteria. The quality of each study was evaluated by the Newcastle-Ottawa Scale (NOS). Outcome measures included the incidence (the primary outcome), gender, age, mortality and complications rate, seasonal variations, geographical area, source, and mechanism of exposure. Results: From 2489 citations, only thirteen studies met the eligibility criteria from five different countries. The date of publication of included studies ranged from 2000 to 2017. The mean sample size was 1483 victims. Most of the victims were females (64.24%). The mean age of the victims was 29.6 years old. The overall incidence of COP was estimated to be equal to 13.37 per 100,000 inhabitants per year. Most of the COP events happened in the winter. Gas heaters were the most frequent source. Conclusion: COP is still a significant burden though it is potentially preventable. Policies and obligations need reevaluation, and public awareness should be raised.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".