Bibliographic record
Abstract
BACKGROUND: In the hospital setting, postoperative admission is a key vulnerable moment when patients are at increased risk of medication discrepancies. This study measures the reduction of medication discrepancies associated with a combined intervention of structured pharmacist medication history interviews with assessments in a surgical preadmission clinic and a postoperative medication order form. METHODS: In the Surgical Pharmacist in Preadmission Clinic Evaluation (SPPACE) study, patients who had a preadmission clinic appointment before undergoing surgical procedures were eligible for inclusion. Patients were excluded if they were scheduled for discharge the same day as their surgery. Eligible patients were randomly assigned to the intervention arm (structured pharmacist medication history interview with assessment and generation of a postoperative medication order form) or to the standard care arm (nurse-conducted medication histories and surgeon-generated medication orders). The primary end point was the number of patients with at least 1 postoperative medication discrepancy related to home medications. RESULTS: Between April 19, 2005, and June 3, 2005, a total of 464 patients were enrolled in the study, of which 227 and 237 patients were randomized to the intervention and standard care arms, respectively. In the intervention arm, 41 (20.3%) of 202 patients had at least 1 postoperative medication discrepancy related to home medications, compared with 86 (40.2%) of 214 patients in the standard care arm (P<.001). In the intervention arm, 26 (12.9%) of 202 patients had at least 1 postoperative medication discrepancy with the potential to cause possible or probable harm, compared with 64 (29.9%) of 214 patients in the standard care arm (P<.001). These were mostly omissions of reordering home medications. CONCLUSION: A combined intervention of pharmacist medication assessments and a postoperative medication order form can reduce postoperative medication discrepancies related to home medications.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".