The Effect of Churn on “Community Viral Load” in a Well-Defined Regional Population
Bibliographic record
Abstract
BACKGROUND: The concept of community viral load (CVL) was introduced to quantify the pool of transmissible HIV within a community and to monitor the potential impact of highly active antiretroviral therapy (HAART) on reducing new infections. The implications of churn (patient movement in/out of care in a community) on CVL have not been studied. METHODS: The annual CVL was determined in the entire geographic HIV population receiving care in southern Alberta from 2001 to 2010; the CVL for specific subpopulations was analyzed for 2009. CVL was determined for patients under continuous care, newly diagnosed, new to the region, moved away, returned, and lost to follow-up (LTFU). Viral loads (VLs) <50 or <200 copies per milliliter were deemed undetectable and suppressed, respectively. The mean VL per patient and total VL were used to determine CVL. RESULTS: From 2001 to 2010, the HAART uptake for all patients increased from 62% to 81%, undetectability from 32% to 66%, and suppression from 49% to 72%. The annual total CVL however did not vary significantly after 2003. Incidence rates for new locally diagnosed infections increased from 4.4 to 5.8/100,000 per year. In 2009, newly diagnosed HIV patients (6.6%) contributed 37.5% to the CVL, whereas patients transferring in/out of the region or lost to follow-up contributed 33% to the CVL. Patients in continuous care (79% of all patients) contributed 29.5% to the total CVL. CONCLUSIONS: Increasing HAART coverage did not reduce the CVL or reduce new HIV diagnoses in our population. The effect of churn significantly limited CVL use as a measure for evaluating the impact of HAART in reducing HIV transmissions in our 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".