Prediction of Health Preference Values from CD4 Counts in Individuals with HIV
Bibliographic record
Abstract
BACKGROUND: A common measure of health benefit in technology assessments is the quality-adjusted life year, which incorporates health preference or utility scores. OBJECTIVE: To build and test a predictive model using CD4 counts to derive health preference scores. DESIGN: Predictive modeling. Setting. Ontario HIV Treatment Network Cohort Study. Measurement. The relationship between HUI3-derived health preference score and HIV health status measured by CD4 count was examined by a regression model. Additional independent variables considered included age, time since HIV diagnosis, AIDS-defining condition, sex, and education level. A polynomial regression model was fit to predict health preference scores. The final model was established using automated backwards stepwise variable elimination using the Akaike information criterion. Tenfold cross-validation was used to assess the model. RESULTS: Data from 841 participants were available. Mean age and time since diagnosis were 46.78 and 11.03 years, respectively. CD4 counts ranged from 2 to 995 cells per mm(3) with 267 (31.75%) individuals having less than 350 cells per mm(3). Mean HUI3 utility score was 0.72 and ranged from -0.25 to 1. The final model retained squared terms for CD4 counts, age, and time since HIV diagnosis and eliminated history of AIDS-defining condition and the nonsquared time since HIV diagnosis. Prediction error was assessed in 14 subgroups using the validation set. Two subgroups had mean prediction errors greater than 0.02. Limitations. All statistical models are limited by the data used to develop and test the model. The model estimates health utility scores primarily through CD4 counts. Therefore, the model may be inappropriate if noninfectious diseases are a significant factor. CONCLUSIONS: Results provide a model for predicting health preference values from CD4 counts.
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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 | 0.003 | 0.013 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".