Seroprevalence of Toxoplasma gondii infection among Iranian pregnant women: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Toxoplasmosis is a great public health concern due to its capacity for prenatal transmission. Serologic studies have reported various estimates for seroprevalence of toxoplasmosis among Iranian pregnant women. Estimation of the pooled prevalence of this infection is necessary for policy-making. AIMS: The aim of this study was to estimate the prevalence of Toxoplasma gondii infection in Iranian pregnant women using systematic review and meta-analysis. METHODS: We searched national and international databases to identify relevant studies. To enhance the search sensitivity, we evaluated all references and interviewed relevant researchers and research centres. The final studies for meta-analysis were selected according to the quality assessment as well as inclusion/exclusion criteria. Because of the heterogeneity of the primary results, random effects models were used to estimate the pooled prevalence of T. gondii. We included 43 studies with a total sample size of 22 644 in the meta-analysis. RESULTS: The pooled seroprevalence of overall toxoplasma infection, IgG antibody and IgM antibody was estimated at 41.3% (95% CI: 35.8-46.8), 39.2% (95% CI: 33.3-45.1) and 4.0% (95% CI: 3.1-4.9) respectively. CONCLUSIONS: Our study showed that a considerable proportion of Iranian pregnant women are at high risk for toxoplasmosis.
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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.033 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".