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Record W4299615121 · doi:10.17615/1frm-m529

Physical therapy vs. internet-based exercise training (PATH-IN) for patients with knee osteoarthritis: study protocol of a randomized controlled trial

2019· article· en· W4299615121 on OpenAlexaboutno aff
Quinn I Williams, Todd A. Schwartz, Kelli D. Allen, Bernadette C Benas, Leigh F. Callahan, Carla H. Hill, Yvonne M. Golightly, Michael T. Gross, Alexander Gunn, John H. Cantrell

Bibliographic record

VenueUNC Libraries · 2019
Typearticle
Languageen
FieldHealth Professions
TopicProblem Solving Skills Development
Canadian institutionsnot available
FundersUniversity of North Carolina at Chapel HillCenters for Disease Control and PreventionH2020 European Research CouncilUniversity of Wisconsin-Madison
KeywordsPhysical therapyRandomized controlled trialOsteoarthritisMedicinePhysical medicine and rehabilitationProtocol (science)Alternative medicineInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background Physical activity improves pain and function among individuals with knee osteoarthritis (OA), but most people with this condition are inactive. Physical therapists play a key role in helping people with knee OA to increase appropriate physical activity. However, health care access issues, financial constraints, and other factors impede some patients from receiving physical therapy (PT) for knee OA. A need exists to develop and evaluate other methods to provide physical activity instruction and support to people with knee OA. This study is examining the effectiveness of an internet-based exercise training (IBET) program designed for knee OA, designed by physical therapists and other clinicians. Methods/Design This is a randomized controlled trial of 350 participants with symptomatic knee OA, allocated to three groups: IBET, standard PT, and a wait list (WL) control group (in a 2:2:1 ratio, respectively). The study was funded by the Patient Centered Outcomes Research Institute, which conducted a peer review of the proposal. The IBET program provides patients with a tailored exercise program (based on functional level, symptoms, and current activity), video demonstrations of exercises, and guidance for appropriate exercise progression. The PT group receives up to 8 individual visits with a physical therapist, mirroring standard practice for knee OA and with an emphasis on a home exercise program. Outcomes are assessed at baseline, 4 months (primary time point) and 12 months (to assess maintenance of treatment effects). The primary outcome is the Western Ontario and McMaster Universities Osteoarthritis Index, and secondary outcomes include objective physical function, satisfaction with physical function, physical activity, depressive symptoms and global assessment of change. Linear mixed models will be used to compare both the IBET and standard PT groups to the WL control group, examine whether IBET is non-inferior to PT (a treatment that has an established evidence base for knee OA), and explore whether participant characteristics are associated with differential effects of IBET and/or standard PT. This research is in compliance with the Helsinki Declaration and was approved by the Institutional Review Board of the University of North Carolina at Chapel Hill. Discussion The IBET program could be disseminated widely at relatively low cost and could be an important resource for helping patients with knee OA to adopt and maintain appropriate physical activity. This trial will provide an important evaluation of the effectiveness of this IBET program for knee OA. Trial registration NCT02312713

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)0.023
metaresearch head score (Gemma)0.021
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 score0.054
Threshold uncertainty score0.179

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.021
Meta-epidemiology (narrow)0.0070.003
Meta-epidemiology (broad)0.0160.007
Bibliometrics0.0030.004
Science and technology studies0.0030.004
Scholarly communication0.0050.004
Open science0.0040.002
Research integrity0.0070.007
Insufficient payload (model declined to judge)0.0540.008

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 head0.022
GPT teacher head0.322
Teacher spread0.300 · 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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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