A Multicenter, Longitudinal Cohort Study of Cryptococcosis in Human Immunodeficiency Virus–negative People in the United States
Bibliographic record
Abstract
BACKGROUND: Cryptococcosis is increasingly recognized in people without human immunodeficiency virus (HIV). METHODS: A multicenter, prospective cohort study was performed in 25 US centers. Consenting patients were prospectively followed for ≤2 years. Neurological morbidities were assessed with longitudinal event depiction and functional scores (Montreal Cognitive Assessment [MoCA]). Risks of death were analyzed using Cox regression. RESULTS: One hundred forty-five subjects were enrolled. Most were male (95; 65.5%) and had immunosuppression (120; 82.8%), including solid organ transplant (SOT; 33.8%), autoimmunity (15.9%), and hematologic malignancies (11.7%). Disease involved the central nervous system (CNS) in 71 subjects (49%). Fever was uncommon, documented in 40 (27.8%) subjects, and absence was associated with diagnostic delay (mean: 48.2 vs 16.5 days; P = .007). Abnormal MoCA scores (<26) were predictive of CNS disease; low scores (<22) were associated with poor long-term cognition. Longitudinal event depiction demonstrated frequent complications in people with CNS disease; 25 subjects (35.2%) required >1 lumbar puncture and 8 (11.3%) required ventriculostomies. In multivariable models, older age (>60 years) was associated with higher risks of death (hazard ratio [HR], 2.14; 95% confidence interval [CI], 1.05-4.38; P = .036), and lower risks were noted with underlying hematologic malignancy (HR, 0.29; 95% CI, 0.09-0.98; P = .05) and prior SOT (HR, 0.153; 95% CI, 0.05-0.44; P = .001). CONCLUSIONS: Despite aggressive antifungal therapies, outcomes of CNS cryptococcosis in people without HIV are characterized by substantial long-term neurological sequelae. Studies are needed to understand mechanism(s) of cognitive decline and to enable better treatment algorithms.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".