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Record W2610746960 · doi:10.1097/mej.0000000000000467

Review of existing measurement tools to assess spinal motion during prehospital immobilization

2017· review· en· W2610746960 on OpenAlexfundno aff
Jeronimo Weerts, Lars Schier, Hendrik Schmidt, Michael Kreinest

Bibliographic record

VenueEuropean Journal of Emergency Medicine · 2017
Typereview
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsnot available
FundersChina Academy of Engineering PhysicsCanadian Association of Emergency Physicians
KeywordsInclinometerMedicineModalitiesInertial measurement unitAccelerometerSpinal surgeryStrain gaugeMedical physicsComputer sciencePhysical medicine and rehabilitationEngineeringSurgeryArtificial intelligenceElectrical engineering

Abstract

fetched live from OpenAlex

The quantification of spinal movement to investigate the efficacy of prehospital immobilization devices and techniques remains difficult. Therefore, we aim to systematically review the literature on reported measurement tools applicable within this research field. A keyword literature search of relevant articles was performed using the database of PubMed including international literature published in English between January 2010 and December 2015. Only studies describing methods applicable to estimate spinal movement during prehospital immobilization were included. Six measurement tools were found that have either been used (goniometer/inclinometer, imaging modalities, electromagnetic systems, and optoelectronic systems) or have the potential to be used (inertial measurement units and a combination of strain gauge technology and accelerometers) in this research field. Novel devices can assess spinal motion during prehospital care including extrication, application of immobilization devices, and transportation from the site of the accident to the final destination, and therefore can be considered for usage.

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)0.009
metaresearch head score (Gemma)0.015
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.570
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.436
GPT teacher head0.472
Teacher spread0.036 · 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.

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

Citations13
Published2017
Admission routes1
Has abstractyes

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