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Record W4389584842 · doi:10.17118/11143/20705

Determination of 3D lumbar spine kinematics by musculoskeletal ultrasound: a preliminary study of validation

2023· article· en· W4389584842 on OpenAlexaff
Mohammad Reza Effatparvar, Marc‐Olivier St‐Pierre, Félix-Antoine Lavoie, Omar Ringa, Stéphane Sobczak

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMedical Imaging and Analysis
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsKinematicsLumbar spineComputer scienceUltrasoundLumbarMedicineBiomedical engineeringRadiologyPhysicsSurgery

Abstract

fetched live from OpenAlex

Lumbar spine 3D kinematics determination is currently based on the bone reconstructions from CT-scan and MRI, which are harmful or expensive.Meanwhile, ultrasound is an alternative with less disadvantages, and has been recently used in lumbar spine 3D modelling.This study intends to apply ultrasound-based models to determine the lumbar spine kinematics and compare it with the CT-scan-based result.In this regard, a human lumbar spine was dissected, and technical markers (TM) (Metallic spheres, ⌀ =4.6mm) were glued to the costiforms and spinous processes from L1 to L5, and median and lateral crests of the S1.Then, after fixing the specimen in a neutral position, musculoskeletal ultrasound (MSU) and CT-scan images were collected.Several image processing techniques were applied, and the ultrasound and CT-scan images were separately reconstructed in 3D.In the next step, to visualize the lumbar spine movement, first, different positions including neutral, full and mid positions in flexion-extension, side bending and axial rotation were applied on the specimen and discrete kinematics were defined by collecting the centroid of the TMs, using a 3D digitizer (Hexagon Absolute Arm, error of measurement (mm): 0.008).Afterwards, the locations of the TMsdigitizer were registered to the TMs on the 3D reconstructions using lhpFusionBox software.After having the motion, to determine the intervertebral kinematics, the top of the costiform processes and the most distal points of the spinous processes where virtually palpated on each level to build local anatomical reference system on CT-Scan and MSU 3D models.Z-axis was defined by the costiform processes landmarks, the X-axis was defined as the orthogonal axis to the Z-axis through the spinous process landmarks, and the Y-axis was perpendicular to X and Z axes.Finally, using Euler angles, the intervertebral kinematics were measured and compared for both, the ultrasound and CT-scan based models.The anatomical motion components, including the flexion-extension, side bending and axial rotation, were defined around the Z, X and Y-axes, respectively.

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)0.003
metaresearch head score (Gemma)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.009
GPT teacher head0.263
Teacher spread0.254 · 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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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