Observational Study of the Effect of Patient Outreach on Return to Care: The Earlier the Better
Bibliographic record
Abstract
BACKGROUND: The burden of HIV remains heaviest in resource-limited settings, where problems of losses to care, silent transfers, gaps in care, and incomplete mortality ascertainment have been recognized. METHODS: Patients in care at Academic Model Providing Access to Healthcare (AMPATH) clinics from 2001-2011 were included in this retrospective observational study. Patients missing an appointment were traced by trained staff; those found alive were counseled to return to care (RTC). Relative hazards of RTC were estimated among those having a true gap: missing a clinic appointment and confirmed as neither dead nor receiving care elsewhere. Sample-based multiple imputation accounted for missing vital status. RESULTS: Among 34,522 patients lost to clinic, 15,331 (44.4%) had a true gap per outreach, 2754 (8.0%) were deceased, and 837 (2.4%) had documented transfers. Of 15,600 (45.2%) remaining without active ascertainment, 8762 (56.2%) with later RTC were assumed to have a true gap. Adjusted cause-specific hazard ratios (aHRs) showed early outreach (a ≤8-day window, defined by grid-search approach) had twice the hazard for RTC vs. those without (aHR = 2.06; P < 0.001). HRs for RTC were lower the later the outreach effort after disengagement (aHR = 0.86 per unit increase in time; P < 0.001). Older age, female sex (vs. male), antiretroviral therapy use (vs. none), and HIV status disclosure (vs. none) were also associated with greater likelihood of RTC, and higher enrollment CD4 count with lower likelihood of RTC. CONCLUSION: Patient outreach efforts have a positive impact on patient RTC, regardless of when undertaken, but particularly soon after the patient misses an appointment.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".