P-167. New epidemiological routes of Coccidioidomycosis in Mexico – the extension of this pathogen to new areas
Bibliographic record
Abstract
Abstract Background Due to specific growing conditions, Coccidioidomycosis is a fungal disease typically found in northern Mexico, near California or Arizona. However, due to climate change, there has been an increase in cases in non-endemic areas. In this study, we describe areas where cases of Coccidioidomycosis have been reported, which were previously not known to have this disease. Image of a map of Mexico that highlights both high-risk and low-risk areas. Dots represent origin and migration. Blue circles represent areas where patients lived while yellow represents areas where people migrated. The reason for migration was not studied. Pink circles represent those without travelling. Methods We developed a registry of Coccidioidomycosis cases, which collects data on sociodemographic, travel, and clinical conditions, including tomography, pathology, and outcomes. The patients were categorized into groups based on migration and geographical living area, and we collected data from physical or electronic health records. Results Between 1991 and 2023, we diagnosed 122 patients with coccidioidomycosis. The most common comorbidities were diabetes mellitus (41%) and overweight (24%). Forty-eight patients (39%) living in endemic areas had high-risk working conditions, such as construction, archaeology, and topography. Diagnosis was made using culture in 79.5% of cases, serology in 54%, and biopsy in 51%. CT scans showed predominant nodular (77%) and cavitary lesions (61%). Surprisingly, 46.7% of patients had no risk factors, such as travelling or living in endemic areas. 29.5% of patients had a history of migration as a risk factor to a high-risk area. The average time from symptom onset to diagnosis was 150 days (IC95 61-518 days). The patients were divided into four groups based on their risk factors. 8 (6.5%) lived and travelled in high-risk areas, 19 (15.5%) lived in high-risk areas without migration, 36 (29.5%) lived in low-risk, travelling to high-risk, and 57(46.7%) lived in low-risk and did not travel. Conclusion This division through risk factors highlights areas not known to be at risk, with patients without a history of travelling presenting with Coccidioidomycosis. We estimate an increasing number of fungal infections due to climate change, characterized by increased drought in some areas. As with other diseases, diagnosing Coccidioidomycosis outside of endemic areas should raise awareness of its expansion, and healthcare workers should consider it as a possible differential diagnosis. It is crucial to have this area known to consider resources for treatment. Disclosures Carlos Flores Nunez, PHD, Pfizer: Grant/Research Support
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".