Statewide Implementation of Universal Third-Trimester Repeat HIV Testing in Illinois
Bibliographic record
Abstract
OBJECTIVE: This article aims to assess statewide uptake of HIV repeat testing in the first 2 years after the implementation of an amendment to the Illinois Perinatal HIV Prevention Act (IPHPA) mandating universal repeat HIV testing in the third trimester. STUDY DESIGN: This is a retrospective, population-based study of all birthing individuals in Illinois (2018-2019). Data were collected using the state-mandated closed system of perinatal HIV test reporting. We evaluated the incidence of mother-infant pairs with negative early tests and repeat third-trimester tests (RTTTs) performed in adherence with the law, as well as the timing of the performance of the RTTTs (outpatient vs. inpatient). Chi-square tests of trend by quarter were performed to ascertain sustainability. RESULTS: = 0.39). Of individuals who presented without RTTTs, 93.5% (2018) and 98.8% (2019) underwent inpatient testing before delivery. CONCLUSION: Implementation of RTTTs in Illinois was rapid, successful, and sustained in its first 2 years. Public health methodologies from Illinois may benefit other states implementing RTTT programs. KEY POINTS: · In 2018, Illinois enacted statewide RTTT for HIV among all parturients.. · In 2019, over 99% of mother-infant dyads had documentation of both early and repeat HIV testing before hospital discharge.. · Implementation of repeat third-trimester HIV testing in Illinois was rapid, successful, and sustained in its first 2 years.. · Public health methodologies from Illinois may benefit other states implementing similar programs..
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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 | 0.006 |
| 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 | 0.000 |
| 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".