Medication Reconciliation during Admission at University Hospital
Bibliographic record
Abstract
INTRODUCTION: Medication reconciliation is the process of comparing the most accurate list of all medications that a patient is taking with the list of prescription drugs within the healthcare system while considering the patient’s allergies and history of side effects. OBJECTIVES: To reconcile medication upon the patients’ admission to a university hospital in the municipality of Campo Grande, Mato Grosso do Sul, Brazil. METHOD: A prospective, cross-sectional study was conducted between June 2018 and May 2019 at the medical clinic unit of an university hospital. Discrepancies observed between the prescription and the best possible medication history were classified as intentional discrepancy; undocumented intentional; and unintentional. RESULTS: A total of 1,134 discrepancies were found between home-based drugs and those prescribed upon admission to the MCU. Among the discrepancies, 815 (72%) were intentional, 89 (8%) were undocumented intentional, and 230 (20%) were unintentional. The number of consultation sources and the number of medicines in use at home showed significant correlation with the occurrence of unintentional discrepancies (p = 0.039 and p = 0.008, respectively). A total of 318 pharmaceutical interventions were performed, 230 related to unintended discrepancies. Of these, 138 (60%) interventions were not accepted. CONCLUSION: The study verified the high frequency of drug omission, conferring treatment interruption and the need for pharmaceutical assistance of polymedicated patients.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".