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Record W2770607212 · doi:10.1186/s41077-017-0057-y

Reducing the impact of intensive care unit mattress compressibility during CPR: a simulation-based study

2017· article· en· W2770607212 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAdvances in Simulation · 2017
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsQueen's UniversityUniversity of British ColumbiaUniversity of British Columbia HospitalAlberta Children's HospitalUniversity of Calgary
FundersCumming School of Medicine, University of CalgaryAlberta Children's Hospital FoundationAlberta Children's Hospital Research InstituteChildren's Hospital Foundation
KeywordsCompression (physics)MedicineIntensive care unitLinear regressionStatisticsIntensive care medicineMathematicsMaterials science

Abstract

fetched live from OpenAlex

BACKGROUND: The depth of chest compression (CC) during cardiac arrest is associated with patient survival and good neurological outcomes. Previous studies showed that mattress compression can alter the amount of CCs given with adequate depth. We aim to quantify the amount of mattress compressibility on two types of ICU mattresses and explore the effect of memory foam mattress use and a backboard on mattress compression depth and effect of feedback source on effective compression depth. METHODS: The study utilizes a cross-sectional self-control study design. Participants working in the pediatric intensive care unit (PICU) performed 1 min of CC on a manikin in each of the following four conditions: (i) typical ICU mattress; (ii) typical ICU mattress with a CPR backboard; (iii) memory foam ICU mattress; and (iv) memory foam ICU mattress with a CPR backboard, using two different sources of real-time feedback: (a) external accelerometer sensor device measuring total compression depth and (b) internal light sensor measuring effective compression depth only. CPR quality was concurrently measured by these two devices. The differences of the two measures (mattress compression depth) were summarized and compared using multilevel linear regression models. Effective compression depths with different sources of feedback were compared with a multilevel linear regression model. RESULTS: The mean mattress compression depth varied from 24.6 to 47.7 mm, with percentage of depletion from 31.2 to 47.5%. Both use of memory foam mattress (mean difference, MD 11.7 mm, 95%CI 4.8-18.5 mm) and use of backboard (MD 11.6 mm, 95% CI 9.0-14.3 mm) significantly minimized the mattress compressibility. Use of internal light sensor as source of feedback improved effective CC depth by 7-14 mm, compared with external accelerometer sensor. CONCLUSION: Use of a memory foam mattress and CPR backboard minimizes mattress compressibility, but depletion of compression depth is still substantial. A feedback device measuring sternum-to-spine displacement can significantly improve effective compression depth on a mattress. TRIAL REGISTRATION: Not applicable. This is a mannequin-based simulation research.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.224
Threshold uncertainty score0.320

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.034
GPT teacher head0.423
Teacher spread0.389 · 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