Pharmacist surveillance of adverse drug events
Bibliographic record
Abstract
PURPOSE: The incidence of adverse drug events (ADEs), preventable ADEs, and potential ADEs was determined using pharmacist surveillance. Drug classes associated with ADEs were also identified. METHODS: The study was conducted in a 30-bed hospital ward of a Canadian teaching hospital between April 28, 2003, and May 26, 2003. All patients admitted to the general medicine service were eligible for study enrollment. A pharmacist performed surveillance to identify new or worsening symptoms, critical laboratory values, and medication errors. Surveillance consisted of daily communications with staff, daily chart reviews for all inpatients, and investigation of spontaneous incident reports. Data were collected to describe all identified outcomes. This information was rated independently by two clinicians to determine if the outcome was an ADE, a preventable ADE, or a potential ADE. Descriptive statistics were used to calculate outcome rates, which were reported as events per 100 patient-days. RESULTS: During 543 patient-days of observation, 24 ADEs occurred (4.4 per 100 patient-days), of which 14 were preventable (2.6 per 100 patient-days); 13 potential ADEs also occurred (2.4 per 100 patient-days). Of all ADEs, 3 (13%) were life threatening, 11 (46%) were serious, and 10 (42%) were significant. The 24 ADEs were associated with nine different drug classes. Four drug classes accounted for 17 ADEs (71%): antidiabetic agents, antibiotics, glucocorticoids, and sedatives and hypnotics. CONCLUSION: Pharmacist surveillance revealed that 4.4 ADEs occurred per 100 patient-days, over half of which were preventable. All preventable and potential ADEs occurred during the ordering and administration stages of medication delivery.
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 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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".