ReviewEmerging infectious diseases2022
Cross-Sectional Study of Clinical Predictors of Coccidioidomycosis, Arizona, USA.
Review in Emerging infectious diseases, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 10 citations in OpenAlex.
- Primary Cutaneous Coccidioidomycosis Presenting on the Leg of an Immunocompetent Patient: A Case Report.Case reports in infectious diseases · 2026Article
- When should coccidioidomycosis be in the differential?ASM case reports · 2025Article
- Extensive erythematous plaques of fungal origin in an overseas student: Cutaneous manifestation of coccidioidomycosis.Medical mycology case reports · 2024Article
- Musculoskeletal manifestations of lower-extremity coccidioidomycosis: a case series.Journal of bone and joint infection · 2024Article
- Diagnosis and Treatment of Pulmonary Coccidioidomycosis and Paracoccidioidomycosis.Journal of fungi (Basel, Switzerland) · 2023Review
- Improving Early Recognition of Coccidioidomycosis in Urgent Care Clinics: Analysis of an Implemented Education Program.Open forum infectious diseases · 2023Article
- Surgical Resection to Treat a Japanese Patient with Pulmonary Coccidioidomycosis.Infection and drug resistance · 2023Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Demographic and clinical indicators have been described to support identification of coccidioidomycosis; however, the interplay of these conditions has not been explored in a clinical setting. In 2019, we enrolled 392 participants in a cross-sectional study for suspected coccidioidomycosis in emergency departments and inpatient units in Coccidioides-endemic regions. We aimed to develop a predictive model among participants with suspected coccidioidomycosis. We applied a least absolute shrinkage and selection operator to specific coccidioidomycosis predictors and developed univariable and multivariable logistic regression models. Univariable models identified elevated eosinophil count as a statistically significant predictive feature of coccidioidomycosis in both inpatient and outpatient settings. Our multivariable outpatient model also identified rash (adjusted odds ratio 9.74 [95% CI 1.03-92.24]; p = 0.047) as a predictor. Our results suggest preliminary support for developing a coccidioidomycosis prediction model for use in clinical settings.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.