A call for standardized national guidelines on QT/QTc monitoring in Canada
Bibliographic record
Abstract
Background: With QT-prolonging drugs being trialed for the treatment of COVID-19, national health associations allude to the importance of proficient QT interval assessment. However, in Canada there is no policy in place that clearly identifies a single method for routine QT monitoring. Aim: To demonstrate the need for a clear Canadian guideline for the measurement of the QT/QTc interval and to advocate for a standardized approach to education. Methods: This paper uses a medical anthropological approach to scale this practice gap from the individual provider to the institutions that govern practice and education. Nurses and emergency medical personnel from hospitals across Canada were polled with questionnaires on their confidence and knowledge of assessing the QT/QTc interval. We seek to identify causes for the widespread lack of confidence that goes beyond the context of nursing and is interdisciplinary in nature. Findings: Of the 292 participants who were polled, roughly 75% report measuring the QT interval. However, more than 50% of participants are not confident in their measurement. Although critical care nurses report the highest levels of confidence, the rate of correct answers amongst the whole of participants on knowledge-based questions is shockingly low (only nine percent attempted to provide a value for the QTc; 34% of those who were unsure of the normal QTc say they were not taught). Ninety percent of participants report they do not analyze the QTc, with critical care nurses accounting for 34% of participants. Conclusion: The lack of consensus on a QTc formula and the absence of clear guidelines on this well-documented issue exacerbate the continued gap in practice observed in our findings. We urge leading organizations to create a national guideline that supports a standardized approach to QT/QTc measurement that can be taught to and used by not only critical care nurses, but everyone in healthcare who provides cardiac monitoring.
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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 | 0.077 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.013 | 0.008 |
| Research integrity | 0.017 | 0.026 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".