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Using Electrical Impedance Tomography in an Experimental Model of Weighted Restraint

2020· article· en· W3082388253 on OpenAlexaff
Mark Campbell, Malitela Mapani, Symon Stowe, Jeff W. Dawson, Andy Adler

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicRestraint-Related Deaths
Canadian institutionsCarleton University
Fundersnot available
KeywordsElectrical impedance tomographyVentilation (architecture)BreathingLung volumesPopulationTidal volumeComputed tomographyAsphyxiaMedicinePhysical medicine and rehabilitationLungTomographyAnesthesiaSurgeryEngineeringRadiologyRespiratory systemAnatomyInternal medicine

Abstract

fetched live from OpenAlex

One restraint technique used by police and paramedical personnel is to apply weight to a prone subject. There is concern that the weight and posture cause breathing difficulties and that restraint asphyxia could contribute to rare, inexplicable arrest-related deaths. Previous studies on restraint asphyxia have used global measures of breathing, which are less sensitive to ventilation changes than other methods. We present a methodology for monitoring individual adaptations to the conditions present in weighted restraint using electrical impedance tomography, which can image the changing distribution of ventilation over time. Results from a pilot study of seven subjects indicated that loss of lung reserve volume was a common consequence of weighted restraint. Our results imply that in more extreme scenarios in which the full weight of one or more officers is applied to a subject during recovery from strenuous activity, weighted restraint may augment risk to the subject. Finally, subjects in the restraint posture of hands behind their heads on average had larger tidal volumes during recovery than subjects with hands behind their backs or at their sides, suggesting this posture permitted deeper breathing and may be preferred in practice, though further study in a larger population is needed.

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 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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.810
Threshold uncertainty score0.461

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.082
GPT teacher head0.346
Teacher spread0.264 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations1
Published2020
Admission routes1
Has abstractyes

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