Use of the Patient Generated Index to Identify Physical Health Challenges Among People Living with HIV: A Cross-Sectional Study
Bibliographic record
Abstract
Many people living with HIV experience physical health challenges including mobility problems, pain, and fatigue. Purpose: To estimate how many people living with HIV identify physical health challenges as important using the patient generated index (PGI). Secondary Objectives: (1) Identify factors associated with reporting physical health challenges; (2) Identify relationships between reporting physical health challenges and standardized health-related quality of life (HRQOL) items; and (3) Estimate the extent to which reporting a physical health challenge explains downstream HRQOL outcomes. Method: Cross-sectional data came from a large Canadian cohort. We administered the PGI and three standardized HRQOL measures. PGI text threads were coded according to the World Health Organization's International Classification of Functioning, Disability, and Health. Regression, discriminant analysis, and chi-square tests were used. Results: Of 865 participants, 248 [28.7%; 95% CI (25.7%, 31.8%)] reported a physical health challenge on the PGI. Participants with better pain (OR: 0.81, 95% CI: 0.71, 0.90) and vitality (OR: 0.71, 95% CI: 0.63, 0.80) by 20 points had lower odds of reporting a physical health challenge. Those who reported a physical health challenge had significantly lower HRQOL on some standardized items. Conclusions: The PGI is well-suited to identify the physical challenges of people living with HIV.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".