https://www.bmj.com/content/372/bmj.n71.
Bibliographic record
Abstract
<div> Background and purpose Snakebites are a neglected public health concern, particularly in tropical regions, causing significant morbidity and mortality. Despite Iran’s high snakebite burden, epidemiological data remain inconsistent. This systematic review and meta-analysis aim to provide estimates of snakebite incidence and geographical distribution across Iranian provinces. Methods A comprehensive search was conducted in PubMed/MEDLINE, Scopus, Web of Science, Embase, Google Scholar, and Persian databases (Magiran, SID) up to February 2025. Observational studies reporting snakebite incidence in Iran were included. Two independent reviewers screened studies, extracted data, and assessed bias using the Newcastle-Ottawa Scale (NOS). A random-effects meta-analysis was performed, with heterogeneity evaluated via I². Meta-regression analyzed temporal trends. Results Of 618 initially identified studies, 8 met the inclusion criteria. This meta-analysis found Iran’s overall snakebite incidence to be 31.89 cases per 100,000 population (95% CI: 16.58-47.20), with extreme regional variation (0.14-295.45). Males showed a significantly higher incidence (108.34) than females (66.79). Geographic analysis revealed the highest rates in southeastern (109.68) and southwestern (116.04) regions, and the lowest in northwestern (4.30) and northern (4.05) areas. Meta-regression indicated a significant temporal increase in incidence (β = 0.035, p < 0.001). High heterogeneity (I² ≥ 99.8%) suggests additional underlying factors influence snakebite distribution. Conclusion Snakebite incidence in Iran exhibits marked geographical and gender disparities, with an upward temporal trend. These findings highlight the need for targeted prevention strategies, improved antivenom access, and enhanced surveillance in high-risk provinces. </div>
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.062 | 0.005 |
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; both teacher heads agree on what is shown here.
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".