ArticleAEM education and training2021
Using natural language processing to compare task-specific verbal cues in coached versus noncoached cardiac arrest teams during simulated pediatrics resuscitation.
Article in AEM education and training, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed, 8 citations in OpenAlex.
- Predicting Critical Outcomes in Suspected Cardiopulmonary Emergencies Using Dispatch Narratives: Temporal Validation Study.Journal of medical Internet research · 2026Article
- Artificial Intelligence in cardiopulmonary resuscitation training - A scoping review.Resuscitation plus · 2026Review
- Artificial intelligence in resuscitation: a scoping review.Resuscitation plus · 2025Review
- Cardiopulmonary resuscitation coaching for resuscitation teams: A systematic review.Resuscitation plus · 2025Review
- Using natural language processing to compare task-specific verbal cues in coached versus noncoached cardiac arrest teams during simulated pediatrics resuscitation.AEM education and training · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesCoaches improve cardiopulmonary (CPR) outcomes in real-world and simulated settings. To explore verbal feedback that targets CPR quality, we used natural language processing (NLP) methodologies on transcripts from a published pediatric randomized trial (coach vs. no coach in simulated CPR). Study objectives included determining any differences by trial arm in (1) overall communication and (2) metrics over minutes of CPR and (3) exploring overall frequencies and temporal patterns according to degrees of CPR excellence.
methodsA human-generated transcription service produced 40 team transcripts. Automated text search with manual review assigned semantic category; word count; and presence of verbal cues for general CPR, compression depth or rate, or positive feedback to transcript utterances. Resulting cue counts per minute (CPM) were corresponded to CPR quality based on compression rate and depth per minute. CPMs were compared across trial arms and over the 18 min of CPR. Adaptation to excellence was analyzed across four patterns of CPR excellence determined by k-shape methods.
resultsOverall coached teams experienced more rate-directive, depth-directive, and positive verbal cues compared with noncoached teams. The frequency of coaches' depth cues changed over minutes of CPR, indicating adaptation. In coached teams, the number of depth-directive cues differed among the four patterns of CPR excellence. Noncoached teams experienced fewer utterances by type, with no adaptation over time or to CPR performance.
conclusionNLP extracted verbal metrics and their patterns in resuscitation sessions provides insight into communication patterns and skills used by CPR coaches and other team members. This could help to further optimize CPR training, feedback, excellence, and outcomes.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.