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Record W2006792713 · doi:10.1055/s-0028-1100916

Accuracy and reliability of MRI quantitative measurements to assess spinal cord compression in cervical spondylotic myelopathy: a prospective study

2010· article· en· W2006792713 on OpenAlexaff
Alina Karpova, Sorin C. Craciunas, Soo-Yong Chua, Doron Rabin, Sean R. Smith, Michael G. Fehlings

Bibliographic record

VenueEvidence-Based Spine-Care Journal · 2010
Typearticle
Languageen
FieldMedicine
TopicCervical and Thoracic Myelopathy
Canadian institutionsToronto Western Hospital
Fundersnot available
KeywordsMedicineMagnetic resonance imagingSagittal planeSpinal cord compressionRadiologySpinal stenosisSpinal cordMyelopathyAsymptomaticNuclear medicineLumbarSurgery

Abstract

fetched live from OpenAlex

STUDY TYPE: Reliability study Introduction: Cervical spondylotic myelopathy (CSM) is the most common spinal cord disorder in persons more than 55 years old. Despite multiple neuroimaging approaches proposed to quantify the spinal cord compromise in CSM patients, magnetic resonance imaging (MRI) remains the procedure of choice by providing helpful information for clinical decision making, determining optimal subpopulations for treatment, and selecting the optimal treatment strategies. However, the validity, reliability, and accuracy of the MRI quantitative measurements have not yet been addressed. OBJECTIVE: To assess the intra- and inter-observer reliability of MRI quantitative measurements of the spinal cord compromise in CSM patients. METHODS: Seventeen CSM patients (13 male) of mean age 54.5 years old were selected from the AOSpine North America database. The patients had different combinations of stenotic levels (1-4 levels) and the clinical severity (range mJOA baseline: 8-18). Asymptomatic or previous surgically treated CSM, active infection, neoplastic disease, rheumatoid arthritis, ankylosing spondylitis, trauma, or concomitant lumbar stenosis were excluded. The patients underwent preoperative MRI using 1.5T (15 patients) and 3T (two patients) scanner, including mid-sagittal T1-weighted, axial and mid-sagittal T2-weighted series. MRI data were analyzed (Mango 2.0 software; Multi-Image Analysis GUI) by four blind raters in three different sessions. Four measurements were analysed: transverse area (TA) (Figure 1), compression ratio (CR) (Figure 2), maximal canal compromise (MCC), and maximal spinal cord compression (MSCC) (Figure 3). The differences for each measurement were evaluated using mixed-effect ANOVA models (ratter, session, ratter x session). The intra- and inter-rater reliability was evaluated with intraclass correlation coefficients (ICC) (Figure 4). Figure 1 Transverse area (TA)Figure 2 Compression ratio (CR = AP/W)Figure 3 Maximal canal compromise (MCC), and maximal spinal cord compression (MSCC). MCC(%) = 1-[Dx/(Da+Db)/2] × 100%; MSCC(%) = 1-[dx/(da+db)/2] × 100%Figure 4 Intraclass correlation coefficients (ICC) Results: The principal findings were: (i) for TA (71.48 ± 12.99mm2), the intra-rater agreement was 0.97 (95% CI, range 0.94-0.99) and the inter-rater agreement was 0.76 (95% CI, range 0.49-0.90); (ii) for CR (0.35 ± 0.04%), 0.94 (95% CI, range 0.88-0.98), and 0.79 (95% CI, range 0.57-0.91) respectively; (iii) for MCC (83.21 ± 2.08%), 0.95 (95% CI, range 0.89-0.98), and 0.64 (95% CI, range 0.28-0.85) respectively; and (iv) for MSCC (82.87 ± 1.52%), 0.93 (95% CI, range 0.86-0.97), and 0.84 (95% CI, range 0.65-0.93) respectively. CONCLUSIONS: Our data suggest that three out of four measurements (TA, CR and MSCC) have acceptable intra- and interreliability coefficients (ICC > 0.75). However, for the maximal canal compromise measure, although the intrareliability was acceptable, the inter-rater reliability was not acceptable (0.64). Based on this study, we recommend that three MRI measures: transverse area, compression ratio and maximal spinal cord compression should be used in the imaging assessment of the spinal cord in CSM patients.

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)0.002
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
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 integrity0.0000.001
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 head0.114
GPT teacher head0.408
Teacher spread0.294 · 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.

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".

Quick stats

Citations12
Published2010
Admission routes1
Has abstractyes

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