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Record W4286798670 · doi:10.32598/irj.20.1.1510.1

Pronator Teres Reflex and the Diagnosis of C6 and C7 Radiculopathy

2022· article· en· W4286798670 on OpenAlexaff
Arash Babaei-Ghazani, Negar Aflakian, Hamid Reza Fadavi, Ali Babashahi, Maziar Azar, Fariba Afshari-Azar, Hosnieh Soleymanzadeh, Mathieu Boudier‐Revéret, Bina Eftekharsadat

Bibliographic record

VenueIranian Rehabilitation Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicNerve Injury and Rehabilitation
Canadian institutionsUniversité de Montréal
FundersIran University of Medical Sciences
KeywordsReflexCervical radiculopathyH-reflexMedicineLikelihood ratios in diagnostic testingElectromyographyMagnetic resonance imagingNeck painPhysical medicine and rehabilitationPhysical examinationPhysical therapyNerve rootPredictive valueRadiologyAnesthesiaSurgeryCervical spineInternal medicinePathology

Abstract

fetched live from OpenAlex

Objectives: Neck roots lesions are among the etiologies of cervical and arm pain. A detailed patient evaluation could assist the diagnosis, reduce imaging requests, and promote the treatment of cervical pain. We tried to estimate the value of pronator teres reflex in C6 and C7 roots irritation. Methods: The present study comprises 118 participants, including 56 patients with C6 and C7 lesions and 62 normal controls. The reliability and usefulness of this reflex in C6 and C7 roots lesions were compared to positive electromyography and imaging with magnetic resonance. Results: The sensitivity, specificity, positive predictive value, and negative predictive value for pronator teres reflex were 36.4%, 13.6%, 64.8%, and 4.6%, respectively. Discussion: This reflex can be considered an additional reflex during the physical examination for C6 and C7 nerve roots injury, but its diagnostic value for C6 and C7 radiculopathy is unreliable to be used for screening purposes.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.227
Threshold uncertainty score0.287

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.010
GPT teacher head0.278
Teacher spread0.267 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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