Prognosis of fungal infection of central nervous system in HIV-infected patients: a retrospective study of 77 patients in Ukraine
Bibliographic record
Abstract
Introduction:We aimed to describe the epidemiological, clinical, laboratory characteristics, and outcomes of central nervous system (CNS) mycosis in patients with human immunodeficiency virus (HIV) and to determine characteristics associated with a higher risk of death.Retrospective data from 77 case histories of HIV-infected patients with neurological symptoms caused by various fungi including Candida and Cryptococcus in Dnipro, Ukraine, were analysed as a case-control study with 40 deceased individuals considered as cases and 37 patients with favourable outcome (survivors) considered as controls.Material and methods: Fungi in cerebrospinal fluid (CSF) were detected with traditional culture methods.Multivariate analysis used (1) binary logistic regression with survivor/dead as a dependent variable and (2) a classification and regression tree (CRT method).Results: A combination of fungal infection with other infections of CNS (dual and triple coinfection) was diagnosed in most cases (n = 53, 68.8%), while the proportion of co-infection was somewhat lower among survivors (59.5%).Clinical manifestations were non-specific.Risk of death was higher among those with tuberculosis (AOR = 2.7, 95% CI: 1.0-7.5)and lower among those infected with Epstein-Barr virus (EBV) (AOR = 0.3, 95% CI: 0.1-1.0)and among patients on ART (AOR = 0.2, 95% CI: 0.1-0.8).Risk of death significantly decreased over time.The classification tree shows that among HIV-mycosis neurological patients not on ART with tuberculosis, the risk of death constituted 75%, while among patients on ART with EBV-infection, all patients survived.Conclusions: Opportunistic mycoses remain an important clinical challenge among immuno-compromised patients especially those who were diagnosed with HIV late, failed to get antiretroviral therapy, and developed tuberculosis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 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 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".