Medication Reconciliation during Admission at University Hospital
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it