Pharmaceutical Care for HIV Patients on Directly Observed Therapy
Bibliographic record
Abstract
BACKGROUND: Inner-city patients infected with HIV can be a challenging group to treat. Homelessness, mental illness, substance abuse, and hepatitis C infection may serve as barriers to effective treatment. A multidisciplinary team including the pharmacist can impact upon the delivery of care to the inner-city HIV patient population. OBJECTIVE: To describe the implementation and provision of pharmaceutical care to inner-city patients taking directly observed therapy (DOT), as well as drug-related problems (DRPs) and their respective outcomes. METHODS: Pharmaceutical care, including the prospective identification and management of DRPs, was provided by a clinical pharmacist. RESULTS: Fifty-seven patients were followed over a 14-month period. Overall, 149 DRPs were identified and >95% were resolved. Those included (1) adverse effects (n = 56; gastrointestinal, central nervous system effects, allergies, laboratory abnormalities), (2) drug interactions (n = 32), (3) drugs indicated for comorbidities (n = 24; safety in pregnancy, tuberculosis, Pneumocystis carinii pneumonia prophylaxis, oral candidiasis, herpes zoster, nutritional supplements), (4) adherence issues (n = 20; altering timing of medication, changing formulation, decreasing pill burden), (5) drugs no longer indicated (n = 10; opportunistic infection prophylaxis, treatment of primary infection), and (6) dosage adjustment (n = 7) for weight and renal insufficiency. CONCLUSIONS: In the provision of pharmaceutical care to HIV-infected patients on DOT, an HIV pharmacist significantly contributed to antiretroviral selection, monitoring of drug therapy, and managing DRPs. An HIV pharmacist can assist in promoting patient adherence and improved outcomes in this setting.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".