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Record W4403901987 · doi:10.1016/j.ocarto.2024.100538

Association of cartilage <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:mrow><mml:msub><mml:mi mathvariant="normal">T</mml:mi><mml:mrow><mml:mn>1</mml:mn><mml:mi mathvariant="normal">ρ</mml:mi><mml:mspace width="0.25em"/></mml:mrow></mml:msub></mml:mrow></mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si2.svg"><mml:mrow><mml:msub><mml:mi mathvariant="normal">T</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow></mml:math> relaxation time measurement with hip osteoarthritis progression: A 5-year longitudinal study using voxel-based relaxometry and Z-score normalization

2024· article· lv· W4403901987 on OpenAlexaff
Rafeek Thahakoya, Koren E. Roach, Misung Han, Rupsa Bhattacharjee, Fei Jiang, Johanna Luitjens, Emma Bahroos, Valentina Pedoia, Richard B. Souza, Sharmila Majumdar

Bibliographic record

VenueOsteoarthritis and Cartilage Open · 2024
Typearticle
Languagelv
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsAlberta Bone and Joint Health InstituteUniversity of Calgary
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Institutes of Health
KeywordsRelaxometryOsteoarthritisNormalization (sociology)VoxelWOMACMedicineNuclear medicineMagnetic resonance imagingRadiologyPathology

Abstract

fetched live from OpenAlex

Objective To study the longitudinal changes of cartilage T 1 ρ and T 2 relaxation time measurements in hip-OA patients. Methods A calibration study compared two scanner data, Scanner-1 (GE Discovery MR750 3.0T) with unilateral acquisition protocol and Scanner-2 (GE Signa Premier 3.0T) with bilateral acquisition protocol, using nine subjects(average age ​= ​40.33 ​± ​13.53 years, 5 females), including one hip-OA subject. Quantified parameters from the Scanner-2 were adjusted using voxel-based relaxometry(VBR) and Z-score normalization to reduce the inter-scanner variability. Eighteen hip-OA Subjects (age ​= ​53.11 ​± ​14.96 years, 12 females) were recruited to the longitudinal variability study from 2016, comprising five assessments at 1-year intervals. Baseline to 3rd-year data used unilateral acquisition with Scanner-1, while 4th-year data used bilateral acquisition with Scanner-2. A linear mixed-effects model(LME) assessed trajectory analyses, with acquisition year, age, sex, body mass index(BMI), and Kellgren-Lawrence(KL) score as predictor variables and cartilage mean T 1 ρ and T 2 values as outcomes. Results VBR analysis after Z-score normalization showed that only a few of the whole cartilage voxels had significant differences in T 1 ρ ( femur-2.36 ​% and acetabular-3.23 ​%) and T 2 (femur-2.30 ​% and acetabular-2.94 ​%) values between the scanners. The LME analysis showed that the BMI predictor variable was significantly correlated with the femur T 1 ρ (p ​< ​0.0001) and T 2 (p ​< ​0.0001) and acetabular T 1 ρ (p ​< ​0.0001) and T 2 (p ​< ​0.0001) cartilage region. Conclusion The calibration study demonstrated the effectiveness of VBR and Z-score normalization in reducing inter-scanner variability. The longitudinal study revealed a significant correlation between T 1 ρ and T 2 values of the cartilage and BMI; also the T 1 ρ and T 2 values increased over time in some of the cartilage subregions.

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)0.006
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.898
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
Science and technology studies0.0030.002
Scholarly communication0.0040.004
Open science0.0020.003
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.3260.002

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 head0.020
GPT teacher head0.244
Teacher spread0.224 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2024
Admission routes1
Has abstractyes

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