Development of a Quality Measure to Improve HIV and Syphilis Screening in the Emergency Department: A Modified Delphi Approach
Bibliographic record
Abstract
Emergency department (ED) screening is an important strategy to address the persistent human immunodefiency virus (HIV) epidemic; however, ED screening rates remain low. This study describes the development of a quality measure to improve screening rates for HIV among ED patients. A panel of 10 emergency physicians with expertise in ED-based preventive health and quality measurement employed a 3-round modified Delphi process. The panel reviewed peer-reviewed literature and national guidelines to identify high-risk patient populations; participated in anonymous surveys to rank ED screening approaches based on the impact and feasibility of measurement and practice; and discussed survey rankings in consensus discussions. The highest impact and most feasible group to target for quality measurement were ED patients already undergoing testing for other sexually transmitted infections (ie, "co-screening"). This study successfully designed a quality measure to improve coscreening for HIV in the ED. Implementing this measure could enhance detection and subsequent linkage to care, ultimately contributing to the control of these epidemics. The focus on combining screening for HIV with diagnostic testing for patients with signs and symptoms of sexually transmitted infection aligns with an emerging approach in health care settings that balances expected health impact with feasibility challenges.
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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 | 0.196 | 0.136 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".