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Record W2912815523 · doi:10.1002/jor.24245

Quantifying Complex Micro‐Topography of Degenerated Articular Cartilage Surface by Contrast‐Enhanced Micro‐Computed Tomography and Parametric Analyses

2019· article· en· W2912815523 on OpenAlexaff
Tuomo Ylitalo, Mikko Finnilä, Harpal K. Gahunia, Sakari S. Karhula, Heikki Suhonen, Maarit Valkealahti, Petri Lehenkari, Edward Hæggström, Kenneth P. H. Pritzker, Simo Saarakkala, Heikki J. Nieminen

Bibliographic record

VenueJournal of Orthopaedic Research® · 2019
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsUniversity of TorontoMount Sinai Hospital
FundersBusiness FinlandFP7 Ideas: European Research CouncilOulun YliopistoAcademy of FinlandLuonnontieteiden ja Tekniikan Tutkimuksen ToimikuntaEuropean Commission
KeywordsArticular cartilageComputed tomographyContrast (vision)Articular surfaceTomographyBiomedical engineeringParametric statisticsGeologyMaterials scienceAnatomyMedicinePhysicsMathematicsRadiologyOpticsOsteoarthritisPathology

Abstract

fetched live from OpenAlex

ABSTRACT One of the earliest changes in osteoarthritis (OA) is a surface discontinuity of the articular cartilage (AC), and these surface changes become gradually more complex with OA progression. We recently developed a contrast enhanced micro‐computed tomography (μCT) method for visualizing AC surface in detail. The present study aims to introduce a μCT analysis technique to parameterize these complex AC surface features and to demonstrate the feasibility of using these parameters to quantify degenerated AC surface. Osteochondral plugs (n = 35) extracted from 19 patients undergoing joint surgery were stained with phosphotungstic acid and imaged using μCT. The surface micro‐topography of AC was analyzed with developed method. Standard root mean square roughness (Rq) was calculated as a reference, and the Area Under Curve (AUC) for receiver operating characteristic analysis was used to compare the acquired quantitative parameters with semi‐quantitative visual grading of μCT image stacks. The parameters quantifying the complex micro‐topography of AC surface exhibited good sensitivity and specificity in identifying surface continuity (AUC: 0.93, [0.80 0.99]), fissures (AUC: 0.94, [0.83 0.99]) and fibrillation (AUC: 0.98, [0.88 1.0]). Standard Rq was significantly smaller compared with the complex roughness (CRq) already with mild surface changes with all surface reference parameters − continuity, fibrillation, and fissure sum. Furthermore, only CRq showed a significant difference when comparing the intact surface with lowest fissure sum score. These results indicate that the presented method for evaluating complex AC surfaces exhibit potential to identify early OA changes in superficial AC and is dynamic throughout OA progression. © 2019 The Authors. Journal of Orthopaedic Research® Published by Wiley Periodicals, Inc. on behalf of the Orthopaedic Research Society. Society. 9999:1–12, 2019.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.089
GPT teacher head0.374
Teacher spread0.285 · 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 designBench or experimental
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

Citations11
Published2019
Admission routes1
Has abstractyes

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