Hospitalization due to Adverse Drug Reactions and Drug Interactions before and after HAART
Bibliographic record
Abstract
OBJECTIVE: To characterize and compare the rates of adverse drug reactions (ADRs) and interactions on admission in two, one-year periods: pre-highly active antiretroviral therapy (HAART) (phase 1) and post-HAART (phase 2). DESIGN: Retrospective chart review. SETTING: University-affiliated tertiary care centre. POPULATION STUDIED: HIV-positive patients admitted to hospital. MAIN RESULTS: In phase 1, 436 of 517 admissions, and, in phase 2, 323 of 350 admissions were analyzed. Over 92% of patients were male, with a mean age of 38 years. Significant differences (P<0.05) in the mean length of stay (12.08 versus 10.02 days), the CD4 counts (99.25 versus 129.45) and the number of concurrent diseases (4.20 versus 3.63) were found between phase 1 and 2, respectively. The mean number of medications taken (5.52 versus 5.94) and the rates of hospitalization with ADRs (20.4% versus 21.4%) or interactions (2.5% versus 2.16%) were similar between the two phases. Antiretrovirals were more common in ADR admissions post-HAART (21.3% versus 36.2%), while antiparasitics, psychotherapeutics and antineoplastics were more common pre-HAART. Other classes of drugs involved in both phases were sulphonamides, narcotics, ganciclovir, foscarnet, antimycobacterials and antifungals. ADR causality was possible or probable in more than 80% of cases. Over 60% of ADRs were grades 3 to 4, and about 85% were either the main or contributing reason for admission. About 65% of patients had at least partial recovery at the time of discharge. In phases 1 and 2, 8.9% and 2.9% of admissions,respectively, with ADRs were fatal. CONCLUSIONS: Although hospitalizations with ADRs and interactions were similar in both phases, HAART therapy has had a significant impact on the incidence and nature of ADRs at St Michael's Hospital, Wellesley Central Site, Toronto, Ontario.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".