MétaCan
Menu
Back to cohort
Record W4399257316 · doi:10.1016/j.resplu.2024.100675

Cognitive aids used in simulated resuscitation: A systematic review

2024· review· en· W4399257316 on OpenAlexaff
Sabine Nabecker, Kevin Nation, Elaine Gilfoyle, Cristian Abelairas‐Gómez, Elina Koota, Yiqun Lin, Robert Greif, Natalie Anderson, Farhan Bhanji, Jan Breckwoldt, Adam Cheng, Andrea Cortegiani, Aaron Donoghue, Kathryn Eastwood, Barbara Farquharson, Ming-Ju Hiseih, Ying‐Chih Ko, Kasper Glerup Lauridsen, Yiquin Lin, Andrew Lockey, Tasuku Mastsuyama, Alexander Olaussen, Taylor Sawyer, Sebastian Schnaubelt, Chih‐Wei Yang, Joyce Yeung

Bibliographic record

VenueResuscitation Plus · 2024
Typereview
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsAlberta Children's HospitalUniversity of CalgaryHospital for Sick ChildrenSinai Health SystemUniversity of Toronto
Fundersnot available
KeywordsResuscitationCardiopulmonary resuscitationCognitionMedicinePsychologyIntensive care medicineEmergency medicinePsychiatry

Abstract

fetched live from OpenAlex

To compare the effectiveness of cognitive aid use during resuscitation with no use of cognitive aids on cardiopulmonary resuscitation quality and performance. This systematic review followed the PICOST format. All randomised controlled trials and non-randomised studies evaluating cognitive aid use during (simulated) resuscitation were included in any setting. Unpublished studies were excluded. We did not include studies that reported cognitive aid use during training for resuscitation alone. Medline, Embase and Cochrane databases were searched from inception until July 2019 (updated August 2022, November 2023, and 23 April 2024). We did not search trial registries. Title and abstract screening, full-text screening, data extraction, risk of bias assessment (using RoB2 and ROBINS-I), and certainty of evidence (using GRADE) were performed by two researchers. PRISMA reporting standards were followed, and registration (PROSPERO CRD42020159162, version 19 July 2022) was performed. No funding has been obtained. The literature search identified 5029 citations. After removing 512 duplicates, reviewing the titles and abstracts of the remaining articles yielded 103 articles for full-text review. Hand-searching identified 3 more studies for full-text review. Of these, 29 studies were included in the final analysis. No clinical studies involving patients were identified. The review was limited to indirect evidence from simulation studies only. The results are presented in five different populations: healthcare professionals managing simulated resuscitations in neonates, children, adult advanced life support, and other emergencies; as well as lay providers managing resuscitations. Main outcomes were adherence to protocol or process, adherence to protocol or process assessed by performance score, CPR performance and retention, and feasibility of chatbot guidance. The risk of bias assessment ranged from low to high. Studies in neonatal, paediatric and adult life support delivered by healthcare professionals showed benefits of using cognitive aids, however, some studies evaluating resuscitations by lay providers reported undesirable effects. The performance of a meta-analysis was not possible due to significant methodological heterogeneity. The certainty of evidence was rated as moderate to very low due to serious indirectness, (very) serious risk of bias, serious inconsistency and (very) serious imprecision. Because of the very low certainty evidence from simulation studies, we suggest that cognitive aids should be used by healthcare professionals during resuscitation. In contrast, we do not suggest use of cognitive aids for lay providers, based on low certainty evidence.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.044
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.012
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.044
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0120.010
Bibliometrics0.0070.008
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.119
GPT teacher head0.467
Teacher spread0.347 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations17
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueResuscitation PlusSame topicSimulation-Based Education in HealthcareFrench-language works237,207