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Record W4386699560 · doi:10.1186/s12998-023-00512-1

Spinal manipulation characteristics: a scoping literature review of force-time characteristics

2023· article· en· W4386699560 on OpenAlexaff
Lindsay M. Gorrell, Luana Nyirö, Mégane Pasquier, Isabelle Pagé, Nicola R Heneghan, Petra Schweinhardt, Martin Descarreaux

Bibliographic record

VenueChiropractic & Manual Therapies · 2023
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsCentre intégré universitaire de santé et de services sociaux de la Capitale-NationaleCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalCentre for Interdisciplinary Research in RehabilitationCentre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-JeanUniversité du Québec à Trois-Rivières
FundersUniversität Zürich
KeywordsMedicineChiropracticSpinal manipulationPhysical therapyManual therapyOsteopathyCINAHLSystematic reviewPhysical medicine and rehabilitationRehabilitationBiomechanicsMEDLINEAlternative medicinePsychological intervention

Abstract

fetched live from OpenAlex

BACKGROUND: Spinal manipulation (SM) is a recommended and effective treatment for musculoskeletal disorders. Biomechanical (kinetic) parameters (e.g. preload/peak force, rate of force application and thrust duration) can be measured during SM, quantifying the intervention. Understanding these force-time characteristics is the first step towards identifying possible active ingredient/s responsible for the clinical effectiveness of SM. Few studies have quantified SM force-time characteristics and with considerable heterogeneity evident, interpretation of findings is difficult. The aim of this study was to synthesise the literature describing force-time characteristics of manual SM. METHODS: This scoping literature review is reported following the Preferred Reporting Items for Scoping Reviews (PRISMA-ScR) statement. Databases were searched from inception to October 2022: MEDLINE (Ovid), Embase, CINAHL, ICL, PEDro and Cochrane Library. The following search terms and their derivatives were adapted for each platform: spine, spinal, manipulation, mobilization or mobilisation, musculoskeletal, chiropractic, osteopathy, physiotherapy, naprapathy, force, motor skill, biomechanics, dosage, dose-response, education, performance, psychomotor, back, neck, spine, thoracic, lumbar, pelvic, cervical and sacral. Data were extracted and reported descriptively for the following domains: general study characteristics, number of and characteristics of individuals who delivered/received SM, region treated, equipment used and force-time characteristics of SM. RESULTS: Of 7,607 records identified, 66 (0.9%) fulfilled the eligibility criteria and were included in the analysis. Of these, SM was delivered to the cervical spine in 12 (18.2%), the thoracic spine in 40 (60.6%) and the lumbopelvic spine in 19 (28.8%) studies. In 6 (9.1%) studies, the spinal region was not specified. For SM applied to all spinal regions, force-time characteristics were: preload force (range: 0-671N); peak force (17-1213N); rate of force application (202-8700N/s); time to peak thrust force (12-938ms); and thrust duration (36-2876ms). CONCLUSIONS: Considerable variability in the reported kinetic force-time characteristics of SM exists. Some of this variability is likely due to differences in SM delivery (e.g. different clinicians) and the measurement equipment used to quantify force-time characteristics. However, improved reporting in certain key areas could facilitate more sophisticated syntheses of force-time characteristics data in the future. Such syntheses could provide the foundation upon which dose-response estimates regarding the clinical effectiveness of SM are made.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.783
Threshold uncertainty score0.759

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.026
GPT teacher head0.340
Teacher spread0.314 · 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 designObservational
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

Citations25
Published2023
Admission routes1
Has abstractyes

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