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Record W4393030332 · doi:10.1093/ptj/pzae045

Telerehabilitation in Physical Therapist Practice: A Clinical Practice Guideline From the American Physical Therapy Association

2024· article· en· W4393030332 on OpenAlexaff
Alan Chong W. Lee, Judith E. Deutsch, Lesley Holdsworth, Sandra L. Kaplan, Heidi Kosakowski, Robert Latz, Lydia Lennox McNeary, Jennifer O’Neil, Oscar Ariel Ronzio, Kelly Sanders, Michelle Sigmund-Gaines, Michele Wiley, Trevor Russell

Bibliographic record

VenuePhysical Therapy · 2024
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsUniversity of Ottawa
FundersAmerican Physical Therapy Association
KeywordsGuidelineTelerehabilitationPhysical therapistClinical PracticePhysical therapyMedicineAssociation (psychology)PsychologyPsychotherapistTelemedicineHealth care

Abstract

fetched live from OpenAlex

A clinical practice guideline on telerehabilitation was developed by an American Physical Therapy Association volunteer guideline development group consisting of international physical therapists and physiotherapists, a physician, and a consumer. The guideline was based on systematic reviews of current scientific literature, clinical information, and accepted approaches to telerehabilitation in physical therapist practice. Seven recommendations address the impact of, preparation for, and implementation of telerehabilitation in physical therapist practice. Research recommendations identify current gaps in knowledge. Overall, with shared decision-making between clinicians and patients to inform patients of service delivery options, direct and indirect costs, barriers, and facilitators of telerehabilitation, the evidence supports the use of telerehabilitation by physical therapists for both examination and intervention. The Spanish and Chinese versions of this clinical practice guideline, as well as the French version of the recommendations, are available as supplementary material (Suppl. Materials).

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.941
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.059
GPT teacher head0.503
Teacher spread0.444 · 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 designOther design
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

Citations75
Published2024
Admission routes1
Has abstractyes

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