Symptomatic Cryptococcal Antigenemia Presenting as Early Cryptococcal Meningitis With Negative Cerebral Spinal Fluid Analysis
Bibliographic record
Abstract
BACKGROUND: Individuals with cryptococcal antigenemia are at high risk of developing cryptococcal meningitis if untreated. The progression and timing from asymptomatic infection to cryptococcal meningitis is unclear. We describe a subpopulation of individuals with neurologic symptomatic cryptococcal antigenemia but negative cerebral spinal fluid (CSF) studies. METHODS: We evaluated 1201 human immunodeficiency virus-seropositive individuals hospitalized with suspected meningitis in Kampala and Mbarara, Uganda. Baseline characteristics and clinical outcomes of participants with neurologic-symptomatic cryptococcal antigenemia and negative CSF cryptococcal antigen (CrAg) were compared to participants with confirmed CSF CrAg+ cryptococcal meningitis. Additional CSF testing included microscopy, fungal culture, bacterial culture, tuberculosis culture, multiplex FilmArray polymerase chain reaction (PCR; Biofire), and Xpert MTB/Rif. RESULTS: We found 56% (671/1201) of participants had confirmed CSF CrAg+ cryptococcal meningitis and 4% (54/1201) had neurologic symptomatic cryptococcal antigenemia with negative CSF CrAg. Of those with negative CSF CrAg, 9% (5/54) had Cryptococcus isolated on CSF culture (n = 3) or PCR (n = 2) and 11% (6/54) had confirmed tuberculous meningitis. CSF CrAg-negative patients had lower proportions with CSF pleocytosis (16% vs 26% with ≥5 white cells/μL) and CSF opening pressure >200 mmH2O (16% vs 71%) compared with CSF CrAg-positive patients. No cases of bacterial or viral meningitis were detected by CSF PCR or culture. In-hospital mortality was similar between symptomatic cryptococcal antigenemia (32%) and cryptococcal meningitis (31%; P = .91). CONCLUSIONS: Cryptococcal antigenemia with meningitis symptoms was the third most common meningitis etiology. We postulate this is early cryptococcal meningoencephalitis. Fluconazole monotherapy was suboptimal despite Cryptococcus-negative CSF. Further studies are warranted to understand the clinical course and optimal management of this distinct entity. CLINICAL TRIALS REGISTRATION: NCT01802385.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".