MSG07: An International Cohort Study Comparing Epidemiology and Outcomes of Patients With <i>Cryptococcus neoformans</i> or <i>Cryptococcus gattii</i> Infections
Bibliographic record
Abstract
BACKGROUND: Cryptococcosis due to Cryptococcus neoformans and Cryptococcus gattii varies with geographic region, populations affected, disease manifestations, and severity of infection, which impact treatment. METHODS: We developed a retrospective cohort of patients diagnosed with culture-proven cryptococcosis during 1995-2013 from 5 centers in North America and Australia. We compared underlying diseases, clinical manifestations, treatment, and outcomes in patients with C. gattii or C. neoformans infection. RESULTS: A total of 709 patients (452 C. neoformans; 257 C. gattii) were identified. Mean age was 50.2 years; 61.4% were male; and 52.3% were white. Time to diagnosis was prolonged in C. gattii patients compared with C. neoformans (mean, 52.2 vs 36.0 days; P < .003), and there was a higher proportion of C. gattii patients without underlying disease (40.5% vs 10.2%; P < .0001). Overall, 59% had central nervous system (CNS) infection, with lung (42.5%) and blood (24.5%) being common sites. Pulmonary infection was more common in patients with C. gattii than in those with C. neoformans (60.7% vs 32.1%; P < .0001). CNS or blood infections were more common in C. neoformans-infected patients (P ≤ .0001 for both). Treatment of CNS disease with induction therapy of amphotericin B and flucytosine occurred in 76.4% of patients. Crude 12-month mortality was higher in patients with C. neoformans (28.4% vs 20.2%; odds ratio, 1.56 [95% confidence interval, 1.08-2.26]). CONCLUSIONS: This study emphasizes differences in species-specific epidemiology and outcomes of patients with cryptococcosis, including underlying diseases, site of infection, and mortality. Species identification in patients with cryptococcosis is necessary to discern epidemiologic patterns, guide treatment regimens, and predict clinical progression and outcomes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".