Bibliographic record
Abstract
OBJECTIVES: To review the literature concerning drug-related problems that result in emergency department visits, estimate the frequency of these problems and the rates of hospital admissions, and identify patient risk factors and drugs that are associated with the greatest risk. METHODS: A systematic search of MEDLINE (January 1966-December 2001), EMBASE (January 1980-December 2001), and PubMed (January 1966-December 2001) databases for full reports published in English was performed. The Ottawa Valley Regional Drug Information Service database of nonindexed pharmacy journals also was searched. RESULTS: Data from eight retrospective and four prospective trials retrieved indicated that as many as 28% of all emergency department visits were drug related. Of these, 70% were preventable, and as many as 24% resulted in hospital admission. Drug classes often implicated in drug-related visits to an emergency department were nonsteroidal antiinflammatory drugs, anticonvulsants, antidiabetic drugs, antibiotics, respiratory drugs, hormones, central nervous system drugs, and cardiovascular drugs. Common drug-related problems resulting in emergency department visits were adverse drug reactions, noncompliance, and inappropriate prescribing. CONCLUSION: Drug-related problems are a significant cause of emergency department visits and subsequent resource use. Primary caregivers, such as family physicians and pharmacists, should collaborate more closely to provide and reinforce care plans and monitor patients to prevent drug-related visits to the emergency department and subsequent morbidity and mortality.
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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".