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Record W4416352538 · doi:10.1186/s12998-025-00615-x

The relation between bulk (external) and internal measures of spinal stiffness

2025· article· en· W4416352538 on OpenAlexafffund
Casper Nim, Kenneth A. Weber, Søren O’Neill, Rune Tendal Paulsen, Liam Culmsee-Holm, Evert Onno Wesselink, Yue-Li Sun, Peter Jun, Alexander Breen, Gregory N. Kawchuk

Bibliographic record

VenueChiropractic & Manual Therapies · 2025
Typearticle
Languageen
FieldMedicine
TopicScoliosis diagnosis and treatment
Canadian institutionsUniversity of Alberta
FundersNatural Environment Research CouncilNatural Sciences and Engineering Research Council of Canada
KeywordsStiffnessLumbarBiomechanicsPalpationLumbar vertebraeDirect stiffness method

Abstract

fetched live from OpenAlex

BACKGROUND: While spinal stiffness is thought to be an important factor in the diagnosis and management of various spinal conditions, it is notoriously difficult to measure directly. As a result, clinicians often rely on posteroanterior palpation to estimate bulk stiffness as a proxy for the stiffness of internal spinal tissues. Unfortunately, the validity of this proxy remains uncertain. To investigate this, we posed two key research questions: (1) How do measurements of bulk stiffness correlate with direct measures of spinal stiffness? and (2) Can bulk stiffness measurements be normalized to more accurately reflect internal spinal stiffness? METHODS: This cross-sectional measurement study investigated the relation between bulk and internal spinal stiffness in a young, asymptomatic cohort. Bulk stiffness defined as external resistance of the spine measured through mechanical indentation at the L3 vertebra, while internal spinal stiffness was assessed concurrently using fluoroscopic imaging. Linear regression was used to analyze the relation between bulk and internal spinal stiffness measures. Bulk measures were then normalized using physical measurements (e.g. height, weight) and tissue volume measures of the multifidi obtained by MRI (i.e. muscle volume) and reanalyzed. RESULTS: Twenty-six persons (26) participated, with data from 7 of those being excluded due to fluoroscopic movement artifacts. Unnormalized bulk stiffness was found to correlate poorly with internal spinal stiffness (R2 = 0.1883). Normalization of bulk stiffness using factors such as body weight and multifidus muscle volume did not improve R2 values. Our results were further validated through post hoc analysis, suggesting en bloc movement of the lumbar spine. CONCLUSIONS: Raw bulk spinal stiffness values should not be used as a proxy for internal spinal stiffness as they measure unrelated constructs. Our results may help explain why bulk stiffness measures of the spine may not always align with clinical outcomes. Attempts to normalize bulk spinal stiffness to various human factors such as weight and paravertebral muscle volume did not improve the correlation between bulk spinal stiffness and internal spinal stiffness.

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)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.052
GPT teacher head0.352
Teacher spread0.300 · 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 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

Citations0
Published2025
Admission routes2
Has abstractyes

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