Bibliographic record
Abstract
De Winter et al ( see page 371 ) studied 3594 patients admitted to hospital through an emergency department and found that 59% of patients had a discrepancy between the physician's medication history and a pharmacist technician's structured medication history.1 This result is consistent with over 20 small studies (median sample size 104 patients) showing 61–67% of patients have at least one discrepancy in the medication history at the time of hospital admission.2 There are at least three good reasons to obtain an accurate medication history at the time of hospital admission. First, more than 1 in 9 emergency department visits are due to drug-related adverse events.3 An accurate medication history will be the cornerstone for diagnosing this common problem. Second, medication history errors may result in incorrect medication orders and incorrect treatment during the admission, leading to patient harm. Third, an accurate medication history is the foundation for accurate medication instructions and prescriptions at the time of hospital discharge. Medication reconciliation (Med Rec) at admission is the process of obtaining the best-possible medication history (BPMH), and using this list to provide correct medications to patients at the time of hospital admission. Med Rec at admission is the cornerstone of Med Rec during subsequent transfers and at discharge. Med Rec is a major patient safety priority for safety improvement organisations such as the WHO,4 and hospital accreditors.5 Well-designed medication reconciliation programmes can reduce medication discrepancies6 and potential adverse drug events,7 although there are no studies to show a reduction in preventable adverse drug events. Successful medication reconciliation implementation is a challenge, as evidenced by the Joint Commission's recent decision …
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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 | 0.096 | 0.258 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.024 | 0.009 |
| Science and technology studies | 0.012 | 0.031 |
| Scholarly communication | 0.021 | 0.045 |
| Open science | 0.014 | 0.018 |
| Research integrity | 0.022 | 0.045 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".