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Record W1976868966 · doi:10.2519/jospt.2001.31.2.96

Biomechanical Basis for Stability: An Explanation to Enhance Clinical Utility

2001· review· en· W1976868966 on OpenAlexaff
Stuart M. McGill, Jacek Cholewicki

Bibliographic record

VenueJournal of Orthopaedic and Sports Physical Therapy · 2001
Typereview
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMedicineStability (learning theory)BiomechanicsRehabilitationCore stabilityField (mathematics)Foundation (evidence)Lumbar spinePhysical medicine and rehabilitationManagement sciencePhysical therapyComputer scienceMachine learningSurgeryMathematics

Abstract

fetched live from OpenAlex

The term “stability,” as used in the field of biomechanics, remains undefined in many clinical cases. This fact can impede the design of therapies intended to enhance joint stability. In fact, Fritz et al, in a review on lumbar instability, concluded that, “At present, much controversy exists regarding the proper definition of the condition, the best diagnostic methods, and the most efficacious treatment approaches.” Some progress has been made in the biomechanics field toward the formulation and implementation of stability in musculoskeletal linkages and joints. The purpose of this review is to synthesize and interpret the biomechanical foundation for stability while avoiding mathematical complexity, to demonstrate the notion of stability using specific musculoskeletal examples, and to propose the next logical steps to full utilization of the stability concept for optimal rehabilitation. This review is not intended as a scholarly treatise but rather as a short commentary aimed at providing clinicians with a vantage point for making clinical decisions. Finally, because we are spine biomechanists, and because the original work defining the mechanics of stability of musculoskeletal systems used the spine as an example, this article emphasizes the spine in its examples. J Orthop Sports Phys Ther 2001;31(2):96–100. doi:10.2519/jospt.2001.31.2.96

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)0.003
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.994
Threshold uncertainty score0.938

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.083
GPT teacher head0.435
Teacher spread0.351 · 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 designOther design
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

Citations103
Published2001
Admission routes1
Has abstractyes

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