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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".