Faculty Opinions recommendation of HIV patients with psychiatric disorders are less likely to discontinue HAART.
Bibliographic record
Abstract
OBJECTIVE: We examined whether having a psychiatric disorder among HIV-infected individuals is associated with differential rates of discontinuation of HAART and whether the number of mental health visits impact these rates.DESIGN: This longitudinal study (fiscal year: 2000-2005) used discrete time survival analysis to evaluate time to discontinuation of HAART. The predictor variable was presence of a psychiatric diagnosis (serious mental illness versus depressive disorders versus none).SETTING: Five United States outpatient HIV sites affiliated with the HIV Research Network.PATIENTS: The sample consisted of 4989 patients. The majority was nonwhite (74.0%) and men (71.3%); 24.8% were diagnosed with a depressive disorder, and 9% were diagnosed with serious mental illness.MAIN OUTCOME MEASURES: Time to discontinuation of HAART adjusting for demographic factors, injection drug use history, and nadir CD4 cell count.RESULTS: Relative to those with no psychiatric disorders, the hazard probability for discontinuation of HAART was significantly lower in the first and second years among those with SMI [adjusted odds ratio: first year, 0.57 (0.47-0.69); second year, 0.68 (0.52-0.89)] and in the first year among those with depressive disorders [adjusted odds ratio: first year, 0.61 (0.54-0.69)]. The hazard probabilities did not significantly differ among diagnostic groups in subsequent years. Among those with psychiatric diagnoses, those with six or more mental health visits in a year were significantly less likely to discontinue HAART compared with patients with no mental health visits.CONCLUSION: Individuals with psychiatric disorders were significantly less likely to discontinue HAART in the first and second years of treatment. Mental health visits are associated with decreased risk of discontinuing HAART. PMID: 19617816 Funding information This work was supported by: NIMH NIH HHS, United States Grant ID: R34-MH080630-02 NIMH NIH HHS, United States Grant ID: R01-MH60831 NIA NIH HHS, United States Grant ID: R01 AG026250 NIDA NIH HHS, United States Grant ID: K23-DA019820 NIDA NIH HHS, United States Grant ID: K23-DA019809 NIDA NIH HHS, United States Grant ID: K23 DA019820-04 NIDA NIH HHS, United States Grant ID: K23 DA019820 NIDA NIH HHS, United States Grant ID: K23 DA019809 AHRQ HHS, United States Grant ID: HS016097 PHS HHS, United States Grant ID: 290-01-012 More Less keyboard_arrow_down
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.359 | 0.076 |
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".