Influence of an “Opt-Out” Test Strategy and Patient Factors on Human Immunodeficiency Virus Screening in Pregnancy
Bibliographic record
Abstract
In Brief OBJECTIVE: To estimate both human immunodeficiency virus (HIV) testing acceptance rates in pregnancy using an opt-out policy and patient characteristics influencing acceptance. METHODS: At the first prenatal visit, HIV testing was offered using an opt-out approach. Reasons for refusing testing were explored. Demographic information was collected on all study subjects. RESULTS: In the prospective portion of the study, 1,140 of 1,233 women (92.5%) accepted testing. Race was predictive of accepting HIV testing, with Asian women significantly less likely (odds ratio [OR] 0.4; 95% confidence interval [CI] 0.3–0.6; P<.001) and Hispanic women significantly more likely (OR 6.9; 95% CI 2.2–22.0; P=.001) to be tested. Although English as a first language, country of birth, and insurance status were not significantly associated with acceptance, women who were fluent in English were more likely to be tested (OR 2.0; 95% CI 1.2–3.3; P=.01). Our testing rates were significantly higher than the provincial average. CONCLUSION: Using an opt-out strategy, HIV testing rates in our clinic were significantly higher than the provincial average. Rates were influenced by race and fluency in English. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov, www.clinicaltrials.gov, NCT00393302 LEVEL OF EVIDENCE: II Acceptance of human immunodeficiency virus screening by pregnant women is influenced by testing strategy used (opt-in compared with opt-out) and patient race.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".