The Impact of “Churn” on Plasma HIV Burden Within a Population Under Care
Bibliographic record
Abstract
BACKGROUND: Cross-sectional reporting of viral suppression rates within a population underestimates the community viral load (VL) burden. Longitudinal approaches, while addressing cumulative effects, may still underestimate viral burden if "churn" (movement in and out of care) is not incorporated. We examined the impact of churn on the cumulative community HIV viral burden. METHODS: All HIV+ patients followed in 2016-2017 at the Southern Alberta Clinic (Calgary, Canada) were categorized as follows: (1) in continuous care, (2) newly diagnosed, (3) diagnosed elsewhere transferring care, (4) returning to care, (5) lost-to-follow-up, (6) moved care elsewhere, or (7) died. Patient days were classified by VL as suppressed (≤200copies/ml), unsuppressed (>200 copies/ml), and transmittable (>1500 copies/ml). RESULTS: Of 1934 patients, 78.4% had suppressed VL; 21.4% had ≥1 unsuppressed VL, and 18.7% ≥1 transmittable VL. Of 1 276 507 total patient days in care, 92.1% were spent suppressed, 7.9% unsuppressed (101 459 days), and 6.4% (81 847 days) transmittable. 88.7% of category 1 patients had suppressed VL, 11.3% ≥1 unsuppressed VL, and 8.9% ever a transmittable VL. Of category 2 patients, 90% became suppressed on treatment (mean - 62 days). 38.5% of category 3 patients presented with a transmittable VL. Category 4 and 5 patients combined had high rates of unsuppressed (54.5%) and transmittable (51.2%) VL and, while representing only 6.2% of all patients, they accounted for 37.1% of unsuppressed and 41.5% of all transmittable days. CONCLUSION: Focus on VL of patients continuously in care misses those with unsuppressed and transmittable VL in a community. Patients moving in and out of care pose an underappreciated risk for HIV transmissions.
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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 | 0.015 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".