Think Fungus—Prevention and Control of Fungal Infections
Bibliographic record
Abstract
Reports of human infections with environmental fungi are on the increase throughout the world. Many of these reports describe infections caused by new agents, as well as by traditional agents with new virulence factors or new mechanisms of infection. Fungal infections historically have been underrecognized and difficult to detect, and treatment options are poor. The reasons for their emergence are likely multifactorial. The advent of medical progress–including the wide use of medical hardware, such as central lines; successful management of immunosuppression in patients with transplanted organs; and immunomodulatory agents for treating underlying diseases from cancer to rheumatoid arthritis–has contributed to the increase in fungal infections in immunocompromised hosts. Risk factors such as changes in land use, seasonal migration, international travel, extreme weather, and natural disasters, and the use of azole antifungal agents in large-scale agriculture are believed to underlie many of the increases in communityacquired fungal infections. Because fungal infections are frequently underrecognized and difficult to detect, one of the largest gaps in our understanding of their epidemiology is determining the incidence of disease. In an article in this issue of Emerging Infectious Diseases, Sondermeyer et al. document the incidence and cost of hospitalizations in California caused by Valley fever (coccidioidomycosis), a fungal infection endemic to the southwestern United States and parts of Latin America (1). This article reports that during 2000– 2011, there were>25,000 Valley fever–associated hospitalizations in California and>$2 billion in hospital costs. The rate of hospitalizations increased over the study period from 2.3 to 5.0 cases/100,000 population, a finding that supports other recent publications documenting the increasing incidence of Valley fever in the United States (2,3). Although the reasons for this increase are not well understood, the practical effect of this increasing incidence has been seen in many settings, including the California
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".