Evaluation of accuracy of drug interaction alerts triggered by two electronic medical record systems in primary healthcare
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
This article presents a study to evaluate the accuracy of drug interaction (DI) alerts triggered by two electronic medical record (EMR) systems in primary healthcare. A scenario-based software architecture analysis methodology (SAAM) was used with drug-drug interaction (DDI) pairs in hypothetical patient scenarios. A literature search identified common drugs used in the management of conditions in the elderly population. Three reference programs determined the level of severity of drug interactions, and a common severity rating scale was adapted. The EMR systems showed a limited potential to identify 'severe' clinically significant DDIs and considerable probability for triggering spurious alerts. This may explain the overriding of DI alerts and the interruption of the workflow of users of EMR systems. Reasons for EMR system deficiency included unavailable updates or programming, database functioning discrepancies, and controversies in the clinical evidence.
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 | 0.100 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| 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