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Record W4210516874 · doi:10.3389/fnint.2021.802378

Manual Therapy Research Methods in Animal Models, Focusing on Soft Tissues

2022· article· en· W4210516874 on OpenAlexaff
Geoffrey M. Bove, Susan L. Chapelle, Matthew J. S. Barrigar, Mary F. Barbe

Bibliographic record

VenueFrontiers in Integrative Neuroscience · 2022
Typearticle
Languageen
FieldVeterinary
TopicAnimal testing and alternatives
Canadian institutionsCanadian Memorial Chiropractic College
FundersNational Center for Complementary and Integrative Health
KeywordsPerspective (graphical)Manual therapyAction (physics)PsychologyMedical physicsComputer scienceMedicineManagement scienceEngineering ethicsArtificial intelligenceAlternative medicinePathologyEngineering

Abstract

fetched live from OpenAlex

Manual therapies have been practiced for centuries, yet little research has been performed to understand their efficacy and almost no animal research has been performed to inform mechanisms of action. The methods of manual therapy practice are quite varied and present a challenge for scientists to model the treatments and perform research using rodents. In this perspective we present a descriptive analysis of the complexity of the treatments, highlighting the role of tissue mechanics and physics. With these complexities in mind, we compare using manual therapy as clinically practiced, to attempts to develop machinery to model or mimic manual therapy. We propose that because of the complexities of manual therapy as practiced, having therapists perform the treatments on research animals just as they would on humans is the most scientific approach. Our results using this approach have supported its practicality.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.705
Threshold uncertainty score0.725

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.464
GPT teacher head0.558
Teacher spread0.094 · 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 designSimulation or modeling
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

Citations7
Published2022
Admission routes1
Has abstractyes

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