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Record W4281490143 · doi:10.1080/09638288.2022.2070288

Effectiveness of instrument-assisted soft tissue mobilization for the management of upper body, lower body, and spinal conditions. An updated systematic review with meta-analyses

2022· review· en· W4281490143 on OpenAlexaff
Goris Nazari, Pavlos Bobos, Steve Lu, Stephanie Reischl, Saurab Sharma, Christina Le, Kyle Vader, Nicholas James Held, Joy C. MacDermid

Bibliographic record

VenueDisability and Rehabilitation · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMyofascial pain diagnosis and treatment
Canadian institutionsResearch CanadaUniversity of AlbertaUniversity of TorontoWestern UniversitySt. Michael's HospitalSt. Joseph's HospitalHand and Upper Limb ClinicMcMaster UniversityQueen's University
Fundersnot available
KeywordsMedicineRange of motionPhysical therapyRandomized controlled trialMeta-analysisPhysical medicine and rehabilitationManual therapyAdverse effectSurgeryInternal medicineAlternative medicinePathology

Abstract

fetched live from OpenAlex

PURPOSE: To critically appraise randomized controlled trials (RCTs) on Instrument-Assisted Soft Tissue Mobilisation (IASTM) and quantify the effects of IASTM compared with other treatment in individuals with or without pathologies on function, pain, and range of motion. MATERIALS AND METHODS: We search four electronic databases from January 1999 to January 2022 and included RCTs of healthy participants/athletes and people with upper, lower, or spinal conditions, who received IASTM versus other active treatment for clinical outcomes (function, pain, and range of motion). RESULTS: Forty-six RCTs were considered eligible for data analysis. Effects of IASTM plus other treatment versus other treatment on function and pain intensity were not statistically significant or clinically meaningful (very low quality, SMD -0.28, 95% CI -0.66 to 0.09) and (very low quality, SMD -0.05, 95% CI -0.53 to 0.43) at up to one-year follow-up respectively. No clinically meaningful improvements were found on range of motion outcomes. Out of the 46 included RCTs, only 10 assessed and reported IASTM-related adverse events. CONCLUSION: Evidence of very low-quality certainty does not support the efficacy of IASTM in individuals with or without various pathologies on function, pain, and range of motion in the management of upper body, lower body, or spinal conditions. IMPLICATIONS FOR REHABILITATIONThe included RCTs had a high risk of bias and were assessed as very-low quality evidence for all the included outcomes.IASTM does not lead to clinically meaningful improvements in function, pain, or range of motion in individuals with upper body, lower body, and spinal conditions.The publication of IASTM trials in suspected predatory journals is increasing.The available evidence on IASTM does not support its use to improve function, pain, or range of motion in individuals with upper body, lower body, and spinal conditions.Health care practitioners should consider other evidence-based management strategies (physical activity and exercise) to improve function, pain, or range of motion in individuals with musculoskeletal injuries and disorders.Given the rise of publications on IASTM in suspected predatory journals, health care practitioners should be judicious to examine the legitimacy of a journal when searching for evidence on IASTM treatment technique.

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)0.021
metaresearch head score (Gemma)0.070
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 score0.021
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.070
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0180.028
Bibliometrics0.0090.006
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.382
Teacher spread0.334 · 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

Citations26
Published2022
Admission routes1
Has abstractyes

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