Provider bias in the selection of non-nucleoside reverse transcriptase inhibitor and protease inhibitor-based highly active antiretroviral therapy and HIV treatment outcomes in observational studies
Bibliographic record
Abstract
OBJECTIVE: To compare the characteristics of patients prescribed non-nucleoside reverse transcriptase inhibitors (NNRTI) and protease inhibitors (PI), and evaluate treatment outcomes in a setting in which nevirapine has been preferentially recommended since 1998. METHODS: A population-based analysis of antiretroviral-naive adults who started highly active antiretroviral therapy (HAART) between 1 August 1996 and 31 July 2000, and who were followed until 31 March 2002. We compared baseline characteristics, and evaluated virological responses and mortality. RESULTS: Overall, 439 patients (28.8%) started HAART with NNRTI (94.1% used nevirapine), 100 (6.6%) used a double PI, and 983 (64.6%) used a single PI-based regimen. Substantial differences were observed between the baseline clinical characteristics of these populations. In adjusted analyses, in comparison with single PI therapy, only the use of NNRTI was associated with more rapid HIV-RNA suppression [relative hazard (RH) 1.42; 95% confidence interval (CI) 1.22-1.65; P < 0.001]. A total of 204 deaths were identified in the study population [42 (9.6%) NNRTI; 11 (11%) double PI; 151 (15.4%) single PI, respectively]. In adjusted analysis, NNRTI (RH 1.01; 95% CI 0.71-1.45) and double PI-based HAART (RH 0.74; 95% CI 0.40-1.39) had similar mortality rates to the single PI reference category. CONCLUSION: NNRTI use was associated with more rapid virological suppression, whereas similar rates of rebound and mortality were found. Nevertheless, major baseline differences existed between patients prescribed the various initial regimens. As such, it is likely that similar selection factors may explain why our findings contrast with several non-randomized studies showing worse clinical outcomes of patients prescribed nevirapine.
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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 | 0.138 | 0.297 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".