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Record W1972250513 · doi:10.3109/10903127.2014.936634

Cervical Spine Motion during Transfer and Stabilization Techniques

2014· article· en· W1972250513 on OpenAlexfundno aff
Ian Shrier, Patrick Boissy, Karina Lebel, John Boulay, Eli Segal, J. Scott Delaney, L. Charlene Vacon, Russell Steele

Bibliographic record

VenuePrehospital Emergency Care · 2014
Typearticle
Languageen
FieldMedicine
TopicSpinal Fractures and Fixation Techniques
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsForeheadMedicineLead (geology)Cervical spineHead (geology)Transfer (computing)Trap (plumbing)OrthodonticsSurgeryPhysicsComputer science

Abstract

fetched live from OpenAlex

across lead paramedics to avoid a practice or fatigue effect with repeated trials. We measured relative cervical spine motion between the head and trunk using inertial measurement units placed on the forehead and sternum. Results. On average, total motion was 3.9° less with three assistants compared to one assistant (p = 0.0002), and 2.8° less with the trap squeeze compared to the head squeeze (p = 0.002). There was no interaction between the transfer method and stabilization technique. When examining specific motions in the six directions, the trap squeeze generally produced less lateral flexion and rotation motion but allowed more extension. Examining within paramedic differences, some paramedics were clearly more proficient with the trap squeeze technique and others were clearly more proficient with the head squeeze technique. Conclusion. Paramedics performing a log roll with three assistants created less motion compared to a log roll with only one assistant, and using the trap squeeze stabilization technique resulted in less motion than the head squeeze technique but the clinical relevance of the magnitude remains unclear. However, large individual differences suggest future paramedic training should incorporate both best evidence practice as well as recognition that there may be individual differences between paramedics.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.344
Threshold uncertainty score0.475

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.005
GPT teacher head0.252
Teacher spread0.247 · 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 designObservational
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

Citations22
Published2014
Admission routes1
Has abstractyes

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