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Record W3198426923 · doi:10.1136/bmjopen-2021-053004

Single session compared with multiple sessions of education and exercise for older adults with spinal pain in an advanced practice physiotherapy model of care: protocol for a randomised controlled trial

2021· article· en· W3198426923 on OpenAlexafffundabout
Simon Lafrance, Carlo Santaguida, Kadija Perreault, Brenna Bath, Kednapa Thavorn, Debbie Feldman, Luc J. Hébert, Julio Fernandes, François Desmeules

Bibliographic record

VenueBMJ Open · 2021
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsOttawa HospitalUniversity of OttawaUniversity of SaskatchewanUniversité de MontréalUniversité LavalHôpital du Sacré-Cœur de MontréalHôpital Maisonneuve-RosemontCentre for Interdisciplinary Research in RehabilitationMcGill University Health Centre
FundersFonds de Recherche du Québec - SantéNational Institute on Deafness and Other Communication DisordersCanadian Institutes of Health ResearchRéseau Provincial de Recherche en Adaptation-RéadaptationFondation de l’Hôpital Maisonneuve-Rosemont
KeywordsMedicinePhysical therapyOswestry Disability IndexRandomized controlled trialNeurosurgeryNeck painPatient satisfactionPhysical medicine and rehabilitationLow back painNursingAlternative medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: To assess the effectiveness and cost-effectiveness of a single session compared with multiple sessions of education and exercise for older adults with spinal pain treated conservatively in an advanced practice physiotherapy model of care. METHODS AND ANALYSIS: In this pragmatic randomised controlled trial, 152 older adults (≥65 years old) with neck or back pain initially referred for a consultation in neurosurgery, but treated conservatively, will be recruited through the advanced practice physiotherapy neurosurgery CareAxis programme in the Montreal region (Quebec, Canada). In the CareAxis programme, older patients with spinal pain are triaged by an advance practice physiotherapist and are offered conservative care and only potential surgical candidates are referred to a neurosurgeon. Participants will be randomised into one of two arms: 1-a single session or 2-multiple sessions (6 sessions over 12 weeks) of education and exercise with the advance practice physiotherapist (1:1 ratio). The primary outcome measure will be the Brief Pain Inventory (pain severity and interference subscales). Secondary measures will include self-reported disability (the Neck Disability Index or Oswestry Disability Index), the Pain Catastrophizing Scale, satisfaction with care questionnaires (9-item Visit-specific Satisfaction Questionnaire and MedRisk), and the EQ-5D-5L. Participants' healthcare resources use and related costs will be measured. Outcomes will be collected at baseline and at 6, 12 and 26 weeks after enrolment. Intention-to-treat analyses will be performed, and repeated mixed-model analysis of variance will assess differences between treatment arms. Cost-utility analyses will be conducted from the perspective of the healthcare system. ETHICS AND DISSEMINATION: (FWA00001935 and IRB00002087). Results of this study will be presented to different stakeholders, published in peer-reviewed journals and presented at international conferences. PROTOCOL VERSION: V.4 August 2021. TRIAL REGISTRATION NUMBER: NCT04868591; Pre-results.

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)0.033
metaresearch head score (Gemma)0.039
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.072
Threshold uncertainty score0.242

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0330.039
Meta-epidemiology (narrow)0.0080.004
Meta-epidemiology (broad)0.0190.010
Bibliometrics0.0040.005
Science and technology studies0.0030.004
Scholarly communication0.0060.005
Open science0.0040.003
Research integrity0.0100.010
Insufficient payload (model declined to judge)0.0720.011

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 head0.034
GPT teacher head0.436
Teacher spread0.403 · 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 designRandomized trial
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

Citations3
Published2021
Admission routes3
Has abstractyes

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