Hypothesis-generating study on the effect of the ACLS guidelines on the use of atropine in cardiac arrest at a community hospital
Bibliographic record
Abstract
Matthew Mo Kin Kwok,1,2 Rob Stenstrom,1 Edward Mak,2 Ka Wai Cheung1 1Department of Emergency Medicine, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada; 2Emergency Department, Richmond Hospital, Vancouver Coastal Health, Richmond, BC, Canada Background: Barriers exist in translating clinical practice guidelines into medical management of patients. These barriers result in delay in translating the Advanced Cardiac Life Support (ACLS) guidelines into clinical practice. We conducted a pilot study employing the recommendation change in atropine usage in the 2010 ACLS guideline algorithm to examine the time lag in translating guidelines into medical practice. Methods and results: We completed a retrospective chart review at a community hospital. Study data was derived from cardiac arrest records from the emergency department between January 1, 2009 and December 31, 2013, before and after the publication of the 2010 ACLS guidelines. All cardiac arrests in the form of asystole and/or pulseless electrical activity at some time during resuscitation in patients aged 19 years and older were included in the study. We examined whether atropine was used during the resuscitation. We studied the use of epinephrine as a control. A time versus atropine and a time versus epinephrine usage graphs were generated and examined. Fifty-five resuscitations met inclusion criteria. Although the 2010 ACLS guidelines were first presented in October 2010, we observed that change in atropine use occurred around the summer of 2011. There was no change in the use of epinephrine. Conclusion: Despite several guideline dissemination strategies, a time lag was found in physicians’ adaptation of the ACLS guidelines. Keywords: cardiopulmonary resuscitations, resuscitations, Advanced Cardiac Life Support, guidelines translation, guidelines dissemination
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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 | 0.044 | 0.174 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".