Outcomes of a nurse‐managed service for stable <scp>HIV</scp>‐positive patients in a large <scp>S</scp>outh <scp>A</scp>frican public sector antiretroviral therapy programme
Bibliographic record
Abstract
OBJECTIVES: Models of care utilizing task shifting and decentralization are needed to support growing ART programmes. We compared patient outcomes between a doctor-managed clinic and a nurse-managed down-referral site in Cape Town, South Africa. METHODS: Analysis included all adults who initiated ART between 2002 and 2011 within a large public sector ART service. Stable patients were eligible for down-referral. Outcomes [mortality, loss to follow-up (LTFU), virologic failure] were compared under different models of care using proportional hazards models with time-dependent covariates. RESULTS: Five thousand seven hundred and forty-six patients initiated ART and over 5 years 41% (n = 2341) were down-referred; the median time on ART before down-referral was 1.6 years (interquartile range, 0.9-2.6). The nurse-managed down-referral site reported lower crude rates of mortality, LTFU and virologic failure compared with the doctor-managed clinic. After adjustment, there was no difference in the risk of mortality or virologic failure by model of care. However, patients who were down-referred were more likely to be LTFU than those retained at the doctor-managed site (adjusted hazard ratio, 1.36; 95% CI, 1.09-1.69). Increased levels of LTFU in the nurse-managed vs. doctor-managed service were observed in subgroups of male patients, those with advanced disease at initiation and those who started ART in the early years of the programme. CONCLUSION: Reorganization of ART maintenance by down-referral to nurse-managed services is associated with programme outcomes similar to those achieved using doctor-driven primary care services. Further research is necessary to identify optimal models of care to support long-term retention of patients on ART in resource-limited settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".