Bibliographic record
Abstract
Adrug-related problem exists when a patient experiences a disease or symptoms related to drug therapy. 1 In their model of pharmaceutical care, Strand and colleagues 1 defined 8 categories of drug-related problems: The patient has a medical condition requiring drug therapy (a drug indication) but is not receiving a drug for that indication. The patient has a medical condition for which the wrong drug is being taken. The patient has a medical condition for which too little of the correct drug is being taken. The patient has a medical condition for which too much of the correct drug is being taken. The patient has a medical condition resulting from an adverse drug reaction. The patient has a medical condition resulting from a drug–drug, drug–food, or drug–laboratory interaction. The patient has a medical condition as a result of not receiving the prescribed drug. The patient has a medical condition as a result of taking a drug for which there is no indication. Drug-related problems have become a major public health issue in North America. They are responsible for staggering annual rates of morbidity and mortality and are associated with enormous costs to the health-care system. Which health-care professionals are in the best position to influence this important problem? Can pharmacists exert an impact on the magnitude of drug-related problems? Can hospital pharmacists make a difference?
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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.030 | 0.007 |
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".