An assessment of the relationship between the World Health Organization <scp>HIV</scp> drug resistance early warning indicators and <scp>HIV</scp> drug resistance acquisition
Bibliographic record
Abstract
OBJECTIVES: The World Health Organization (WHO)'s HIV drug resistance (HIVDR) early warning indicators (EWIs) measure antiretroviral therapy (ART)-site factors associated with HIVDR prevention, without HIVDR laboratory testing. We assessed the relationship between EWIs and HIVDR acquisition using data from British Columbia, Canada. METHODS: Eligible patients were ART-naïve, were ≥ 19 years old, had initiated ART between 1 January 2000 and 31 December 2012, had ≥ 15 months of follow-up, and were without transmitted HIVDR. Patients were followed for acquired HIVDR until 31 March 2014, the last contact date, or death. We built logistic regression models to assess the associations and predictive ability of individual indicators and of the EWI Score (the number of indicators for which a patient did not meet the criteria) on HIVDR acquisition (to any class of HIVDR, lamivudine (3TC)/emtricitabine (FTC), nonnucleoside reverse transcriptase inhibitors (NNRTIs), nucleoside reverse transcriptase inhibitors (NRTIs) or protease inhibitors (PIs)]). RESULTS: All explored EWIs were associated with at least one class of HIVDR, with the exception of 'ART prescribing practices'. We observed a dose-response relationship between acquiring HIVDR to any antiretroviral class and an increasing EWI score in our predictive logistic regression model. The area under the curve was 0.848 (excellent discrimination). The adjusted odds ratios for acquiring any class of HIVDR for an EWI score of 1, 2 and ≥ 3 versus 0 were 2.30 [95% confidence Interval (CI) 1.21-4.38], 3.35 (95% CI: 1.86-6.03) and 7.26 (95% CI: 4.18-12.61), respectively. CONCLUSIONS: Several EWIs were associated with and predictive of HIVDR, supporting the WHO EWIs as a component of the HIVDR prevention method in settings where HIVDR testing is not routinely or widely available.
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".