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Record W3208049387 · doi:10.1093/pch/pxab061.018

24 Reducing vibrations to improve infant patient safety during transportation

2021· article· en· W3208049387 on OpenAlexaff
Laurent Renesme, F. Darwaish, Kim Greenwood, Cheryl Aubertin, James Green, Adrian D. C. Chan, Robert Langlois, Stephanie Redpath

Bibliographic record

VenuePaediatrics & Child Health · 2021
Typearticle
Languageen
FieldMedicine
TopicAutomotive and Human Injury Biomechanics
Canadian institutionsCarleton UniversityChildren's Hospital of Eastern Ontario
Fundersnot available
KeywordsShakerAccelerometerVibrationTruckWhole body vibrationComputer scienceMedicinePhysical medicine and rehabilitationEngineeringAutomotive engineeringAcousticsPhysics

Abstract

fetched live from OpenAlex

Abstract Primary Subject area Neonatal-Perinatal Medicine Background Each year, thousands of newborns are transported by air or ground ambulance to receive specialized medical care. For neurologically immature and physiologically compromised infants, especially preterm infants, the noise and vibration exposure during transport are high despite preventative measures, and may be an important contributor to brain injury risk. Objectives To develop a new tool to investigate vibrations during neonatal transport and mitigation strategies. Design/Methods Proof of concept study including 3 steps: 1) Characterization of the vibrations during transport. Accelerometer sensors placed on different layers of the Neonatal Patient Transport System (NPTS) (neonate manikin, mattress, incubator, deck, stretcher, and vehicle floor) with a variety of ambulance on road tests performed to capture data. 2) Experimentation - A shaker table was used to develop a standardized test environment. Vibration testing was performed, with the entire NPTS mounted on the shaker table. 3) Mitigation - Shaker table tests were repeated using different configurations of mattress and harness types on manikins with different bodyweights. Results 1) Characterization: Road transport exposed the manikin’s head to vibrations that exceeded adult standards. Examining the frequency spectra of the accelerometer signals across different layers of the NPTS suggests that two interfaces, stretcher/vehicle floor and incubator/deck, may be critical for intervention to mitigate the vibrations, as they both showed the highest gains in vibration power. 2) Experimentation: Comparison between the on-road and shaker table tests showed that the shaker table was able to reproduce on-road transportation with acceptable fidelity. The shaker table setup can serve as a standardized environment to explore the impact of several NPTS design variables on vibrations transmitted to the patient. 3) Mitigations: Different mattresses were shown to influence the vibrations experienced by the manikin. The head restraint harness type showed an amplitude reduction of the peak frequency component for all experiment types and for most mattress types compared to a standard 5-point harness. Conclusion Our study demonstrated that: i) vibrations during neonatal transport can exceed adult standards; ii) acceptable fidelity simulation of road conditions can be achieved using a shaker table system; and iii) the most effective approach for vibration mitigation should consider the whole NTPS, instead of focusing solely on the isolette.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.002

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 head0.010
GPT teacher head0.262
Teacher spread0.252 · 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 designNot applicable
Domainnot available
GenreEmpirical

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

Citations0
Published2021
Admission routes1
Has abstractyes

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