Quantitative Magnetic Resonance Imaging Analysis of the Cervical Spine Extensor Muscles: Intrarater and Interrater Reliability of a Novice and an Experienced Rater
Bibliographic record
Abstract
STUDY DESIGN: Reliability study. PURPOSE: To examine the reliability of novice and experienced raters for measurements of the size and composition of the cervical extensor muscles using a thresholding technique. OVERVIEW OF LITERATURE: Although some authors have reported on the dependability of magnetic resonance imaging (MRI) measurements of the cervical muscles, there remains some variability regarding intrarater and interrater reliabilities, and few studies have examined the associated measurement error. Whether the rater's experience noticeably influences the reliability and precision of such measurements has also not been examined. METHODS: A sample of 10 patients with cervical pathologies was selected. Muscle cross-sectional area (CSA), functional cross-sectional area (FCSA), and signal intensity of the cervical extensor muscles were acquired from axial T2-weighted MRIs by a novice and an experienced rater. All measurements were obtained twice, at least 5 days apart, while the raters were blinded to all earlier measurements. RESULTS: Interrater reliability estimates (intraclass correlation coefficients) varied between 0.84 and 0.99 for the novice rater and between 0.94 and 0.99 for the experienced rater, indicating excellent reliability. The standard error of measurement for the novice rater was, however, noticeably higher for all cervical muscle measurements. Most of the interrater estimates showed excellent agreement with the exception of CSA measurement of the semispinalis cervicis at C4-C7 and FCSA measurement of the multifidus and semispinalis cervicis at C4-C7, which showed poor interrater reliability. CONCLUSIONS: The proposed method of investigating cervical muscle measurements was highly reliable; however, novice raters should receive adequate training before using this method for diagnostic, research, and clinical purposes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".