Modifiering och validering av kliniska regler för att identifiera riskordinationer vid Akademiska sjukhuset i Uppsala
Bibliographic record
Abstract
Background: Uppsala University Hospital Sweden is planning to implement a closed loop medication system, with the aim of reducing risk prescriptions from the point of drugs being prescribed to orders being produced and administered. With inspiration from Leuven, an advanced system for pharmaceutical validation; System Assisted Pharmaceutical VALidation (SAPVAL) is planned to be developed. Aim: The aim of the study was to obtain a deeper understanding of clinical rules as an important element for building the SAPVAL system. This study will review and further develop a first set of clinical rules and validate these on the intended study population. Methods: A retrospective cross-sectional study was performed to validate the clinical rules on a study population of 500 patients who were discharged from Uppsala University Hospital between May to July 2020. The clinical rules were applied cross-sectionally based on patient data from the electronic health records. From the total generated alerts, 10 % was randomly selected for assessment of the clinical relevance. Results: The clinical rules generated 893 alerts in 500 patients, of which 84 % alerts still remained two days after the patient was admitted to the hospital or at discharge. From the randomly selected alerts, 26 % were deemed clinically relevant. Conclusions: The developed clinical rules generate a large number of alerts for risk prescriptions for inpatients at Uppsala University hospital. The majority of the alerts remained during the care period and approximately a quarter of them were considered to be clinically relevant to remedy.
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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 | 0.088 | 0.177 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".