MétaCan
Menu
Back to cohort
Record W1970150526 · doi:10.1186/1471-2342-13-30

Quantification of cervical spine muscle fat: a comparison between T1-weighted and multi-echo gradient echo imaging using a variable projection algorithm (VARPRO)

2013· article· en· W1970150526 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBMC Medical Imaging · 2013
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsWestern University
FundersNational Center for Advancing Translational SciencesNational Institutes of HealthNorthwestern University
KeywordsMedicineMagnetic resonance imagingWhiplashNeck painNuclear medicineGradient echoConcordanceCervical vertebraeCervical spineAlgorithmRadiologyAnatomyMathematicsPathologySurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Previous data using T1-weighted MRI demonstrated neck muscle fat infiltration (MFI) in patients with poor functional recovery following whiplash. Such findings do not occur in those with milder symptoms of whiplash, chronic non-traumatic neck pain or healthy controls, suggesting traumatic factors play a role. Muscle degeneration could potentially represent a quantifiable marker of poor recovery, but the temporal constraints of running a T1-weighted sequence and performing the subsequent analysis for muscle fat may be a barrier for clinical translation. The purpose of this preliminary study was to evaluate, quantify and compare MFI for the cervical multifidus muscles with T1-weighted imaging and a more rapid quantitative 3D multi-echo gradient echo (GRE) Dixon based method in healthy subjects. METHODS: 5 asymptomatic participants with no history of neck pain underwent cervical spine MRI with a Siemens 3 Tesla system. The muscle and fat signal intensities on axial spin-echo T1-weighted images were quantitatively classified for the cervical multifidii from C3-C7, bilaterally. Additional axial GRE Dixon based data for fat and water quantification were used for comparison via paired t-tests. Inter-tester reliability for fat and water measures with GRE images were examined using 1) Pearson's Intra-class correlation coefficient 2) Bland-Altman Plots and 3) Lin's-Concordance Coefficient. P < 0.05 was used to indicate significance. RESULTS: Total mean (SD) MFI (C3-C7) for the multifidii obtained with T1-weighted imaging and GRE were 18.4% (3.3) (range 14-22%) and 18.8% (2.9) (range 15-22%), respectively. The Pearson correlation coefficients for inter-tester reliability on the GRE sequences for the C3-C7 multifidii ranged from .83 - .99, indicating high levels of agreement with segmental MFI measures. Bland-Altman Plots revealed all data points were within 2 SDs and concordance was established between 2-blinded raters, suggesting good agreement between two raters measuring fat and water with GRE imaging. CONCLUSIONS: Results of this preliminary study demonstrate reliability between 2 raters of varying experience for MRI analysis of MFI with 3D GRE MRI. The quantification of MFI for healthy cervical musculature is comparable to T1-weighted images. Inclusion of larger samples of symptomatic data and histological comparison with the reference standard biopsy is warranted.

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.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: none
Teacher disagreement score0.690
Threshold uncertainty score0.787

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.031
GPT teacher head0.331
Teacher spread0.299 · 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