Vertebral Artery Hypoplasia: Prevalence and Reliability of Identifying and Grading its Severity on Magnetic Resonance Imaging Scans
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study is to examine the inter- and intraexaminer reliability of determining the prevalence of vertebral artery hypoplasia on magnetic resonance imaging (MRI) as well as the reliability of assigning a severity grading of mild, moderate, or marked hypoplasia. METHODS: Two chiropractic radiologists independently evaluated the MR images of 131 adult patients retrospectively for visual vertebral artery hypoplasia. Severity of hypoplastic was graded. The side of hypoplasia and sex of the patient were recorded. The process was repeated after 1 month. Descriptive statistics were calculated for prevalence, severity, and sex distribution of hypoplasia. The kappa statistic was calculated for the reliability of detecting and grading the hypoplasia. RESULTS: Interexaminer reliability was substantial for both readings (kappa = 0.68, 83% agreement for the first reading; kappa = 0.75, 86% agreement for the second reading). Interexaminer reliability for grading the severity of asymmetry was substantial (kappa = 0.73, 83% agreement for the first read; kappa = 0.69, 81% agreement for the second reading). Intraexaminer reliability readings provided a kappa of 0.71 (substantial) and 83% agreement for examiner 1. Examiner 2 had a kappa of 0.85 (almost perfect) with 92% agreement. Overall, 57 (43.5%) of the 131 patients demonstrated hypoplasia. Hypoplasia was more common in women (49%) than men (35.8%). Seven arteries demonstrated severe hypoplasia. Six of these 7 patients were women. CONCLUSIONS: Vertebral artery hypoplasia is common and can be reliably diagnosed and categorized on cervical MRI scans. Vertebral artery hypoplasia was more common in women than men in this group of patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".