Assessing Different Partner Notification Methods for Assuring Partner Treatment for Gonorrhea: Looking for the Best Mix of Options
Bibliographic record
Abstract
CONTEXT: Partner notification for gonorrhea is intended to interrupt transmission and to bring people exposed to infection to care. Partner notification may be initiated through public health professionals (disease intervention specialist: DIS referral) or patients (patient referral). In some cases, patients may carry medications or prescriptions for partners (patient-delivered partner therapy: PDPT). OBJECTIVE: To examine how patterns of notifying and treating partners of persons with gonorrhea differ by partner notification approach. DESIGN: From published literature (2005-2012), we extracted 10 estimates of patient referral data from 7 studies (3853 patients, 7490 partners) and 5 estimates of PDPT data from 5 studies (1781 patients, 3125 partners). For DIS referral estimates, we obtained 2010-2012 data from 14 program settings (4581 patients interviewed, 8301 partners). For each approach, we calculated treatment cascades based on the proportion of partners who were notified and treated. We also calculated cascades based on partners notified and treated per patient diagnosed. RESULTS: Proportions of partners notified and treated were, for patient referral, 56% and 34%; for PDPT, 57% and 46%; for DIS referral, 25% and 22%. Notification and treatment estimates for patient referral and PDPT were significantly higher than for DIS referral, but DIS referral was more efficacious than the other methods in assuring treatment among those notified (all Ps < .001). The notification and treatment ratios per patient seen were, for patient referral, 0.96 and 0.61; for PDPT, 0.90 and 0.73; for DIS referral, 0.45 and 0.40. CONCLUSION: Patient-based methods had higher proportions of partners treated overall, but provider referral had the highest proportion treated among those notified. These data may assist programs to align the most efficacious strategies with the most epidemiologically or clinically important cases while assuring the best scalable standard of care for others.
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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 | 0.152 | 0.246 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".