Gender differences in clinical, immunological, and virological outcomes in highly active antiretroviral-treated HIV–HCV coinfected patients
Bibliographic record
Abstract
OBJECTIVE: The influence of biological sex on human immunodeficiency virus (HIV) antiretroviral treatment outcome is not well described in HIV–hepatitis C (HCV) coinfection. METHODS: We assessed patients’ clinical outcomes of HIV–HCV coinfected patients initiating antiretroviral therapy attending the Ottawa Hospital Immunodeficiency Clinic from January 1996 to June 2008. RESULTS: We assessed 144 males and 39 females. Although similar in most baseline characteristics, the CD4 count was higher in females (375 vs 290 cells/μL). Fewer females initiated ritonavir-boosted regimens. The median duration on therapy before interruption or change was longer in males (10 versus 4 months) (odds ratio [OR] 1.40 95% confidence interval: 0.95–2.04; P = 0.09). HIV RNA suppression was frequent (74%) and mean CD4 count achieved robust (over 400 cells/μL) at 6 months, irrespective of sex. The primary reasons for therapy interruption in females and males included: gastrointestinal intolerance (25% vs 19%; P = 0.42); poor adherence (22% vs 15%; P = 0.31); neuropsychiatric symptoms (19% vs 5%; P = 0.003); and lost to follow-up (3% vs 13%; P = 0.08). Seven males (5%) and no females discontinued therapy for liver-specific complications. Death rate was higher in females (23% vs 7%; P = 0.003). CONCLUSION: There are subtle differences in the characteristics of female and male HIV–HCV coinfected patients that influence HIV treatment decisions. The reasons for treatment interruption and change differ by biological sex. This knowledge should be considered when starting HIV therapy and in efforts to improve treatment outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".