Laboratory Measures as Proxies for Primary Care Encounters: Implications for Quantifying Clinical Retention Among HIV-Infected Adults in North America
Bibliographic record
Abstract
Because of limitations in the availability of data on primary care encounters, patient retention in human immunodeficiency virus (HIV) care is often estimated using laboratory measurement dates as proxies for clinical encounters, leading to possible outcome misclassification. This study included 83,041 HIV-infected adults from 14 clinical cohorts in the North American AIDS Cohort Collaboration on Research and Design (NA-ACCORD) who had ≥1 HIV primary care encounters during 2000-2010, contributing 468,816 person-years of follow-up. Encounter-based retention (REB) was defined as ≥2 encounters in a calendar year, ≥90 days apart. Laboratory-based retention (RLB) was defined similarly, using the dates of CD4-positive cell counts or HIV-1 RNA measurements. Percentage of agreement and the κ statistic were used to characterize agreement between RLB and REB. Logistic regression with generalized estimating equations and stabilized inverse-probability-of-selection weights was used to elucidate temporal trends and the discriminatory power of RLB as a predictor of REB, accounting for age, sex, race/ethnicity, primary HIV risk factor, and cohort site as potential confounders. Both REB and RLB increased from 2000 to 2010 (from 67% to 78% and from 65% to 77%, respectively), though REB was higher than RLB throughout (P < 0.01). RLB agreed well with REB (80%-86% agreement; κ = 0.55-0.62, P < 0.01) and had a strong, imperfect ability to discriminate between persons retained and not retained in care by REB (C statistic: C = 0.81, P < 0.05). As a proxy for REB, RLB had a sensitivity and specificity of 84% and 77%, respectively, with misclassification error of 18%.
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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 | 0.042 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".