Promoting HIV indicator condition-guided testing in hospital settings (PROTEST 2.0): study protocol for a multicentre interventional study
Bibliographic record
Abstract
BACKGROUND: Late presentation remains a key barrier towards controlling the HIV epidemic. Indicator conditions (ICs) are those that are AIDS-defining, associated with a prevalence of undiagnosed HIV > 0.1%, or whose clinical management would be impeded if an HIV infection were undiagnosed. IC-guided HIV testing is an effective strategy in identifying undiagnosed HIV, but opportunities for earlier HIV diagnosis through IC-guided testing are being missed. We present a protocol for an interventional study to improve awareness of IC-guided testing and increase HIV testing in patients presenting with ICs in a hospital setting. METHODS: We designed a multicentre interventional study to be implemented at five hospitals in the region of Amsterdam, the Netherlands. Seven ICs were selected for which HIV test ratios (proportion of patients with an IC tested for HIV) will be measured: tuberculosis, cervical/vulvar cancer or high-grade cervical/vulvar dysplasia, malignant lymphoma, hepatitis B and C, and peripheral neuropathy. Prior to the intervention, a baseline assessment of HIV test ratios across ICs will be performed in eligible patients (IC diagnosed January 2015 through May 2020, ≥18 years, not known HIV positive) and an assessment of barriers and facilitators for HIV testing amongst relevant specialties will be conducted using qualitative (interviews) and quantitative methods (questionnaires). The intervention phase will consist of an educational intervention, including presentation of baseline results as competitive graphical audit and feedback combined with discussion on implementation and opportunities for improvement. The effect of the intervention will be assessed by comparing HIV test ratios of the pre-intervention and post-intervention periods. The primary endpoint is the HIV test ratio within ±3 months of IC diagnosis. Secondary endpoints are the HIV test ratio within ±6 months of diagnosis, ratio ever tested for HIV, HIV positivity percentage, proportion of late presenters and proportion with known HIV status prior to initiating treatment for their IC. DISCUSSION: This protocol presents a strategy aimed at increasing awareness of the benefits of IC-guided testing and increasing HIV testing in patients presenting with ICs in hospital settings to identify undiagnosed HIV in Amsterdam, the Netherlands. TRIAL REGISTRATION: Dutch trial registry: NL7521 . Registered 14 February 2019.
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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 | 0.069 | 0.044 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.059 | 0.013 |
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".