Abstract 42: CPR with a Pulse: Confusion Among Healthcare Providers
Bibliographic record
Abstract
Background: Healthcare providers nationwide are routinely trained in ACLS, a program from the AHA that teaches participants how to manage cardiac arrest events. Recent changes in ACLS teaching have de-emphasized routine pulse checks in an effort to promote uninterrupted chest compressions. We hypothesized that clinicians strictly following the AHA ACLS algorithm may be unsure of the appropriate next action when a resuscitation team member detects a pulse following defibrillation, leading the resuscitator to continue compressions for another two minutes and potentially consider additional medications. Methods: We constructed an internet survey with scenario-based ACLS questions. The survey link was offered to ACLS providers at 4 major medical centers in the U.S. Inclusion criteria consisted of adult staff member of participating institution with ACLS certification. Results: A total of 347 surveys met all criteria and were analyzed. The majority (51%) of responders were between the ages of 26-32, and 59.6% were female. Among the analyzed cohort, 54.2 % of responders stated they would continue compressions (+/- other therapies) when a team member detects a pulse following defibrillation whereas 45.8% chose to cease CPR. Of those who chose to continue CPR in patients with a pulse, 42% selected CPR only, 10% selected epinephrine or vasopressin along with CPR, 20% provided amiodarone, and 29% provided both epinephrine and amiodarone. When stratified by profession, 60.2 % of physician participants chose to continue chest compressions in the presence of a pulse, while 47.2 % of nurse responders chose to continue chest compressions. Conclusion: In this investigation we found that confusion exists as to whether or not CPR and cardiac medications should be continued in the presence of a pulse. Educational initiatives to encourage providers to avoid compressions and medications when a palpable pulse is detected may be necessary.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".