Predictors of outcome in routine care for Cryptococcal meningitis in Western Kenya: lessons for HIV outpatient care in resource-limited settings
Bibliographic record
Abstract
PURPOSE: Cryptococcal meningitis is a leading cause of mortality among HIV-infected individuals in sub-Saharan Africa but little is known about its treatment and outcomes in decentralised HIV outpatient settings. We assessed adherence to treatment guidelines and determined predictors of survival. DESIGN: A computerised laboratory database identified HIV-infected adults with cryptococcal meningitis at Family AIDS Care and Education Services in Nyanza Province, Kenya, between 2005-2009. Medical records were reviewed. Kaplan-Meier survival curves were generated. Bivariate and multivariate Cox proportional hazards models were used to determine associations between key clinical characteristics and survival. RESULTS: Medical records were located for 79% (71/90). Mortality was 38% (27/71) over a median follow-up period of 201 days (IQR: 10-705 days). Adherence to local guidelines for treatment of cryptococcal meningitis was 48% (34/71). Higher body mass index was associated with improved survival (HR: 0.82, 95% CI (0.68 to 0.99)) even after controlling for factors such as age, CD4 cell count, receipt of highly active anti-retroviral therapy, and treatment with any anti-fungal therapy. CONCLUSIONS: Cryptococcal meningitis diagnosed in routine HIV outpatient settings is largely treated as an outpatient and adherence to treatment guidelines is poor. Body mass index is a critical independent predictor of outcome. Additional research to determine the most effective strategies to reduce premature mortality is urgently needed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".