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Record W4387373188 · doi:10.1016/j.apmr.2023.09.017

One and Done? The Effectiveness of a Single Session of Physiotherapy Compared With Multiple Sessions to Reduce Pain and Improve Function and Quality of Life in Patients With a Musculoskeletal Disorder: A Systematic Review With Meta-analyses

2023· review· en· W4387373188 on OpenAlexafffund
Marc-Olivier Dubé, Sarah Dillon, Kevin Gallagher, Jake Ryan, Karen McCreesh

Bibliographic record

VenueArchives of Physical Medicine and Rehabilitation · 2023
Typereview
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversité LavalCentre for Interdisciplinary Research in Rehabilitation
FundersRéseau Provincial de Recherche en Adaptation-RéadaptationPublic Health Agency
KeywordsPhysical therapySession (web analytics)MedicinePhysical medicine and rehabilitationRehabilitationQuality of life (healthcare)NursingComputer science

Abstract

fetched live from OpenAlex

OBJECTIVE: To compare single and multiple physiotherapy sessions to improve pain, function, and quality of life (QoL) in patients with musculoskeletal disorders (MSKDs). DATA SOURCES: AMED, Cinahl, SportsDiscus, Medline, Cochrane Register of Clinical Trials, Physiotherapy Evidence Database, and reference lists. STUDY SELECTION: Randomized controlled trials (RCTs) comparing single and multiple physiotherapy sessions for MSKDs. DATA EXTRACTION: Two reviewers extracted data and assessed risk of bias and certainty of evidence using Cochrane Risk of Bias tool 2.0 and Grading of Recommendation Assessment, Development, and Evaluation. DATA SYNTHESIS: Six RCTs (n=2090) were included (conditions studied: osteoporotic vertebral fracture, neck, knee, and shoulder pain). Meta-analyses with low-certainty evidence showed a significant pain improvement at 6 months in favor of multiple sessions compared with single session interventions (3 RCTs; n=1035; standardized mean difference [SMD]: 0.29; 95% CI: 0.05 to 0.53; P=.02) but this significant difference in pain improvement was not observed at 3 months (4 RCTs; n=1312; SMD: 0.39; 95% CI: -0.11 to 0.89; P=.13) and at 12 months (4 RCTs; n=1266; SMD: -0.05; 95% CI: -0.49 to 0.39; P=.82). Meta-analyses with low-certainty evidence showed no significant differences in function at 3 (4 RCTs; n=1583; SMD: 0.05; 95% CI: -0.11 to 0.21; P=.56), 6 (4 RCTs; n=1538; SMD: 0.06; 95% CI: -0.12 to 0.23; P=.53) and 12 months (4 RCTs; n=1528; SMD: 0.08; 95% CI: -0.08 to 0.25; P=.30) and QoL at 3 (4 RCTs; n=1779; SMD: 0.08; 95% CI: -0.02 to 0.17; P=.12), 6 (3 RCTs; n=1206; SMD: 0.03; 95% CI: -0.08 to 0.14; P=.59), and 12 months (4 RCTs; n=1729; SMD: -0.03; 95% CI: -0.12 to 0.07; P=.58). CONCLUSIONS: Low certainty meta-analyses found no clinically significant differences in pain, function, and QoL between single and multiple physiotherapy sessions for MSKD management for the conditions studied. Future research should compare the cost-effectiveness of those different models of care.

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)0.016
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.024
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.040
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0240.035
Bibliometrics0.0060.005
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0030.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.048
GPT teacher head0.383
Teacher spread0.336 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations17
Published2023
Admission routes2
Has abstractyes

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