Stressing the need for validated measures of cortisol in HIV research: A scoping review
Bibliographic record
Abstract
OBJECTIVES: People living with HIV experience numerous endocrine abnormalities and psychosocial stressors. However, interactions between HIV, cortisol levels, and health outcomes have not been well described among people living with HIV on effective therapy. Furthermore, methods for measuring cortisol are disparate across studies. We describe the literature reporting cortisol levels in people living with HIV, describe methods to measure cortisol, and explore how this relates to health outcomes. METHODS: We searched the PubMed database for articles published in the past 20 years regarding HIV and cortisol with ≥50% of participants on antiretroviral therapies. Articles included observational, case-control, cross-sectional, and randomized controlled trials analyzing cortisol by any method. Studies were excluded if abnormal cortisol was due to medications or other infections. Variables were extracted from selected studies and their quality was assessed using the Newcastle-Ottawa Scale. RESULTS: In total, 19 articles were selected and included, covering the prevalence of abnormal cortisol (n = 4), exercise (n = 4), metabolic syndrome and/or cardiovascular disease (n = 2), mental health and cognition (n = 9), and sex/gender (n = 6). Cortisol was measured in serum (n = 7), saliva (n = 8), urine (n = 2), and hair (n = 3) specimens. Comparisons between people with and without HIV were inconsistent, with some evidence that people with HIV have increased rates of hypocortisolism. Depression and cognitive decline may be associated with cortisol excess, whereas anxiety and metabolic disease may be related to low cortisol; more data are needed to confirm these relationships. CONCLUSIONS: Data on cortisol levels in the era of antiretroviral therapy remain sparse. Future studies should include controls without HIV, appropriately timed sample collection, and consideration of sex/gender and psychosocial factors.
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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 | 0.049 | 0.172 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.026 | 0.021 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".