QTP-MACE Protocol Appendices Aug2024
Bibliographic record
Abstract
Medications which can prolong the QT-interval on an electrocardiogram have been associated with fatal and major non-fatal adverse events affecting the heart (MACE). Despite the severity of the outcome and the very frequent need to prescribe these medications, it is surprisingly unclear which medications actually cause MACE in clinical practice for which patients and under what set of circumstances. Meanwhile computerized alerts regarding these medications are constantly disrupting patient care and may lead to lower quality prescribing and physician burnout. This is an international problem. We have formed the largest hospital electronic medical record (EMR) research network in Canada to be able to address this important patient safety issue. Our combined use of the Ontario GEMINI network and Epic-Dovetale EMR data has huge power because of the nearly 1 million patients, the rich data including medication exposure, outcomes and risk factor determination that is not available elsewhere, and the development of data structure that allows for future international collaborations. Our objectives are to a) investigate whether ‘known’ QT-prolonging medications (QTPmeds) are associated with an increased risk of MACE using advanced time-varying methods, b) use advanced machine learning methods to determine which patients, with which other diseases, medications and lab abnormalities, are more likely to suffer MACE while taking a QTPMed, and c) map our data to OMOP international standards. This project will not only improve patient safety dramatically by improving the accuracy of QTPmeds-related alerts, it will also improve productivity and work satisfaction for thousands of healthcare providers internationally. In addition, the continued expansion and improvement of the largest Canadian hospital EMR research data platform is a huge new opportunity for future high-quality research.
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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 | 0.009 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.705 | 0.382 |
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".