MétaCan
Menu
Back to cohort

Quantification of carpal tunnel morphology using centroid-to-boundary distance shape signatures

2023· article· en· W4362706256 on OpenAlexaff
Drew A. Anderson, Michele Oliver, Karen Gordon

Bibliographic record

VenueMedical Engineering & Physics · 2023
Typearticle
Languageen
FieldComputer Science
TopicOptical measurement and interference techniques
Canadian institutionsUniversity of Guelph
FundersDJO Global
KeywordsCadaveric spasmCentroidFlatteningCarpal tunnel syndromeAmplitudeAnatomyWristBoundary (topology)DorsumMinimum distanceMaterials sciencePhase (matter)Carpal tunnelGeometryPhysicsBiomedical engineeringGeologyMathematicsOpticsMedicineComposite materialSurgeryMathematical analysisStatistics

Abstract

fetched live from OpenAlex

Morphology analysis is valuable to understanding risk factors and the etiology of carpal tunnel (CT) syndrome. The objective of this study was to investigate morphology changes along the length of the CT using shape signatures (SS). Analysis was performed on ten cadaveric specimens in neutral wrist posture. Centroid-to-boundary distance SS were generated for proximal, middle, and distal CT cross-sections. Phase shift and Euclidean distance were quantified relative to a template SS for each specimen. Medial, lateral, palmar, and dorsal peaks were identified on each SS to generate metrics of tunnel width, tunnel depth, peak amplitude, peak angle. Width and depth measures were also performed using previously reported methods to serve as a basis of comparison. The phase shift revealed twisting of 21° between the ends of the tunnel. Distance from the template and width varied significantly over the length of the tunnel, while depth did not. Measures of width and depth using the SS method were consistent with previously reported methods. The SS method afforded the advantage of peak analysis with overall trends of peak amplitude indicating flattening of the tunnel at the proximal and distal ends relative to a rounder shape in the middle.

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)0.000
metaresearch head score (Gemma)0.000
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: none
Teacher disagreement score0.935
Threshold uncertainty score0.467

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.036
GPT teacher head0.277
Teacher spread0.241 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueMedical Engineering & PhysicsSame topicOptical measurement and interference techniquesFrench-language works237,207