Mucocutaneous Manifestations Among HIV-Infected Patients in Madagascar: Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: More than 90% of HIV-infected patients present with at least one mucocutaneous manifestation during the course of their disease. Insufficient data are available regarding dermatologic findings among HIV-infected patients in Madagascar. OBJECTIVE: This study aimed at evaluating the spectrum of mucocutaneous manifestations and their relationship with CD4 cell counts in HIV-infected patients in Madagascar. METHODS: A cross-sectional study on HIV-positive patients attending the Department of Infectious Diseases in the University Hospital of Antananarivo in Madagascar was conducted from January 2013 to March 2020. HIV-positive patients older than 18 years and receiving antiretroviral therapy as well as those awaiting antiretroviral therapy commencement were included. RESULTS: Among 328 patients enrolled in this study, 167 (51%) presented with at least one type of mucocutaneous lesion. Oral candidiasis was the most common presentation, followed by seborrheic dermatitis and Kaposi sarcoma. Decreases in CD4 cell counts were substantially correlated with oral candidiasis, syphilis, and condyloma acuminatum. CONCLUSIONS: According to our findings, oral candidiasis, syphilis, and condyloma acuminatum may serve as clinical indicators for predicting the immune status of patients. As HIV infection progressed and immune function declined, an increase in cutaneous manifestations was observed.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".