MétaCan
Menu
← Back to cohort
Record W4213001805 · doi:10.1136/bmjopen-2021-058190

Analysis of sensorimotor control in people with and without neck pain using inertial sensor technology: study protocol for a 1-year longitudinal prospective observational study

2022· article· en· W4213001805 on OpenAlexaff
Filippo Moggioli, Tomás Pérez-Fernández, Sonia Liébana, Elena Bocos-Corredor, Susan Armijo‐Olivo, Josué Fernández‐Carnero, Rafael Raya, Óscar Rodríguez-López, Cristina Sánchez, Aitor Martín‐Pintado‐Zugasti

Bibliographic record

VenueBMJ Open · 2022
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversity of Alberta
FundersAgencia Estatal de InvestigaciónMinisterio de Ciencia, Innovación y Universidades
KeywordsMedicineNeck painObservational studyPhysical medicine and rehabilitationPhysical therapyPopulationRange of motionAsymptomaticSurgeryInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Neck pain is a very common musculoskeletal disorder associated with high socioeconomic costs derived from work absenteeism and medical expenses. Previous studies have suggested that patients with neck pain of different origins present sensorimotor control impairments compared with the asymptomatic population. However, there is a small number of published studies focusing on these with conflicting results. In addition, the existing methodological limitations highlight the need for more and better quality studies. Moreover, longitudinal studies are necessary to investigate whether changes in pain or disability in individuals with chronic neck pain over time associate with changes in cervical sensorimotor control. METHODS AND ANALYSIS: This is a descriptive, observational, longitudinal, prospective study consecutively enrolling 52 patients with non-specific neck pain and 52 age-matched asymptomatic participants.Intensity of pain, neck disability, duration of symptoms, topography of pain and comorbidities will be registered at baseline. Sensorimotor control variables including active range of motion, movement speed, acceleration, smoothness of motion, head repositioning accuracy and motion coupling patterns will be recorded as primary outcomes by means of inertial sensors during the following tests consecutively performed in two sessions separated by 12 months: (1) kinematics of planar movements, (2) kinematics of the craniocervical flexion movement, (3) kinematics during functional tasks and (4) kinematics of task-oriented neck movements in response to visual targets.Secondary outcomes will include: (1) Regular physical activity levels, (2) Kinesiophobia, (3) Symptoms related to central sensitisation and (4) The usability of the inertial measurement unit sensor technology. ETHICS AND DISSEMINATION: This study was approved by the Research Ethics Committee of CEU San Pablo University (495/21/39). Patients will be recruited after providing written informed consent and they will be able to withdraw their consent at any time. Only the study investigators will have access to the study data. The results will be disseminated through scientific publications, conferences and media. TRIAL REGISTRATION NUMBER: NCT05032911.

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)0.022
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.022
Threshold uncertainty score0.116

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.013
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0020.003
Science and technology studies0.0030.002
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0080.003

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 head0.086
GPT teacher head0.435
Teacher spread0.349 · 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 designObservational
Domainnot available
GenreProtocol

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

Citations9
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueBMJ Open→Same topicMusculoskeletal pain and rehabilitation→French-language works237,207→