Bibliographic record
Abstract
Discovered more than 100 years ago as a human pathogen, the Cryptococcus neoformans–Cryptococcus gattii (C. neoformans–C. gattii) complex has seen a large global resurgence in its association with clinical disease in the last 30 years. First isolated in fermenting peach juice, and identified as a human pathogen in 1894 in a patient with bone lesions, this environmental pathogen has now found niches in soil, trees, birds, and domestic pets. Cryptococcosis is well recognized as an opportunistic infection and was first noted to be associated with reticuloendothelial cancers in the 1950s. Since then, advances in transplant immunology, medical science and surgical techniques have led to increasing numbers of solid organ transplantations (SOT) and hematological stem cell transplantations being performed, and the use of biological immunotherapeutics in increasingly high-risk and older individuals, have contributed to the further rise in cryptococcosis. Globally, however, the major driver for revivification of cryptococcosis is undoubtedly the HIV epidemic, particularly in Sub-Saharan Africa where access to care and antiretroviral therapy remains limited and advanced immunodeficiency, poverty and malnutrition remains the norm. As a zoonotic disease, environmental outbreaks of both human and animal cryptococcosis have been reported, possibly driven by climate change. This is best exemplified by the resurgence of C. gattii infection in Vancouver Island, Canada, and the Pacific Northwest of the United States since 1999. Here we describe how the colliding epidemics of HIV, transplantation and immunologics, climate change and migration have contributed to the rise of cryptococcosis.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".