Burden of fungal infections in Iran
Bibliographic record
Abstract
INTRODUCTION: The number of fungal infections occurring each year in Iran is not known. As the burden of fungal disease is a measure used to assess and compare the relative impact of different type of fungal diseases on populations, we have estimated the burden of fungal diseases in Iran. METHODOLOGY: We estimated the burden of human fungal diseases based on the specific populations at risk, existing epidemiological data in both local and international databases, and modelling previously described by the LIFE program (http://www.LIFE-worldwide.org). RESULTS: Among the population of Iran (79,926,270 in 2016), 6,670,813 (8.3%) individuals are estimated to suffer from a fungal infection each year. A total of 2,791,568 women aged between 15 and 50 years are estimated to suffer from recurrent vulvovaginal candidiasis, annually. In addition, considering the 13.3% prevalence rate of tinea capitis in children, a total of 2,552,624 cases per year are estimated. The estimated burden of invasive aspergillosis in the 3 groups of patients with hematologic malignancy, lung cancer and chronic pulmonary obstructive disease was 6394 (8.0 per 100,000). The estimate for the burden of allergic disease related to fungi including allergic bronchopulmonary aspergillosis, severe asthma with fungal sensitization and allergic fungal rhinosinusitis was 272,095 (340 per 100,000). Based on the 28,663 cases of HIV infection reported, an estimated 900 and 113 cases with pneumocystosis and cryptococcal meningitis are annually anticipated, respectively. CONCLUSION: Our estimates indicate that the importance of fungal infections is high but overlooked in Iran, which warrants further actions by health care authorities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".