Antiretroviral-Related Alopecia in HIV-Infected Patients
Bibliographic record
Abstract
Objective: To review the literature evaluating antiretroviral-related alopecia and to provide guidance on the differential diagnosis and management of this condition. DATA SOURCES: A literature search was performed using PubMed, MEDLINE, Embase, International Pharmaceutical Abstracts (IPA), Cumulative Index to Nursing and Allied Health (CINAHL), and the Cochrane database (through May 2014). Relevant conference abstracts and product monographs were reviewed. Search terms included antiretroviral, individual antiretroviral classes and names, highly active antiretroviral therapy, HIV, AIDS, alopecia, hair, hair loss and drug. STUDY SELECTION AND DATA EXTRACTION: English-language studies and case reports were included. A total of 16 articles and 1 conference abstract were retrieved, with a total of 46 patients with hair loss. DATA SYNTHESIS: The protease inhibitor class, in particular indinavir, was most commonly reported to cause hair loss, followed by the NRTI, lamivudine. The majority of cases presented with alopecia of the scalp alone, with a median time of onset of 2.5 months. Management involved discontinuing the drug in most cases, with at least partial reversal in half the cases. CONCLUSIONS: In antiretroviral-induced alopecia, discontinuation of the suspected agent is the optimal management, and hair regrowth should occur within 1 to 3 months. Management may also include replacing the offending medication with an antiretroviral less likely to cause hair loss. It is essential to rule out other causes of alopecia with a complete patient history, including characterization of the hair loss and assessment of the patient's medical history, medication use, and family history of alopecia.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".