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 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 | 0.009 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".