Provider kinematic strategies during the delivery of spinal manipulation and mobilization: a scoping review of the literature
Bibliographic record
Abstract
BACKGROUND: Spinal manipulation (MAN) and mobilization (MOB) are biomechanically different yet both elicit pain reduction and increased range of motion. Previous investigations have focused on quantifying kinetics (e.g., applied forces) or, recipient kinematics (i.e., movements) during MAN and MOB. While these studies provide valuable information, they do not report on the strategies adopted by providers when performing the complex motor tasks of MAN and MOB. This review sought to synthesise the literature reporting on provider kinematics during the delivery of MAN and MOB. METHODS: This scoping literature review is reported following the Preferred Reporting Items for Scoping Reviews (PRISMA-ScR) statement. MEDLINE (Ovid), PsychINFO, Cochrane Library, Web of Science, Embase, Scopus, PEDro, ICL and CINAHL databases were searched from inception to September 2023 for terms relating to provider kinematics during the delivery of MAN and MOB. Data were extracted and reported descriptively, including: general study characteristics, number and characteristics of individuals who delivered/received MAN and/or MOB, region treated, equipment used and kinematic parameters of the individual delivering the procedure. RESULTS: Of 4,844 records identified, five (0.1%) fulfilled the eligibility criteria and were included in the analysis. Of these, provider kinematics were reported for the delivery of MAN in four (80%) and for the delivery of MOB in one (20%) article. Practitioners applied the procedure in all (100%) and students in one (20%) study. Spinal regions treated were: lumbar (n = 4), thoracic (n = 2) and cervical (n = 1). Data were reported heterogeneously but were most commonly captured using either video or motion capture equipment (n = 4, 80%). The direction of applied force was fully reported in one (20%) and only partially reported (one spinal region) in another study. CONCLUSIONS: There are a small number of studies reporting heterogeneously on provider kinematics during the delivery of MAN and MOB. Clear reporting of the procedure from a biomechanical perspective and of the measurement equipment used could enable future meta-analysis of provider kinematic data, the use of provider kinematic data in the development of technique skills curricula and could feasibly be used to mitigate risk of injury for providers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.020 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".