Poor health-related quality of life in people living with HIV co-infected with SARS-CoV-2 in France (the COVIDHIV study)
Bibliographic record
Abstract
Abstract Background: Among people living with HIV/AIDS (PLWHA), comorbid conditions, including COVID-19, could favor impairment of health-related quality of life (HRQL). This study aimed to assess HRQL and its correlates among PLWHA co-infected with SARS-CoV-2 in France. Methods: This cross-sectional study was conducted using data from PLWHA co-infected with SARS-CoV-2 collected at inclusion in the COVIDHIV cohort study. HRQL was measured using the four dimensions of PROQOL-HIV scale: Physical Health Symptom (PHS), Mental and Cognitive (MC), Social Relationship (SR), Treatment Impact (TI). Factors associated with each dimension were identified using linear regression. Findings: Of the 371 participants included in this study, 64.7% were male, their mean age (SD) was 52(±12) years. The mean (SD) scores of the HRQL dimensions were 76.7(±21.1) for PHS, 79.2(±23.6) for SR, 67.3(±27.4) for MC and 83.9(±16.5) for TI. Better PHS score was associated with being professionally active and good self-perceived knowledge about COVID-19. Having acquired HIV by blood transfusion, stage C of CDC HIV-classification, having received discharge instructions in hospital, and number of self-reported symptoms were associated with worse score in PHS dimension. Living in a couple was associated with better SR score. Having received instructions at hospital discharge, being at stage C of CDC HIV-classification, having acquired HIV by drug injection, number of self-reported symptoms, and self-perceived vulnerable to COVID-19 were associated with worse score in SR dimension. Better score in the MC dimension was associated with being professionally active, and being born in metropolitan France, while being female, having detectable HIV viral-load, having received instructions at hospital discharge, self-perceived vulnerable to COVID-19, smoking, and number of self-reported symptoms were associated with worse score in MC dimension. Being born in metropolitan France, having acquired HIV in homosexual or bisexual relationships were associated with better score in TI dimension. Having detectable HIV viral-load, psychiatric disorders, and number of self-reported symptoms were associated with worse score in TI dimension. Conclusion: Among PLWHIV co-infected with SARS-CoV-2, the scores of HRQL were impaired, particularly in the MC dimension. The knowledge of associated factors can help clinicians for better care of this population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".