Diagnostic separation of conventional ⩾50% carotid stenosis and near-occlusion with phase-contrast MRI
Bibliographic record
Abstract
INTRODUCTION: The aim of this study was to assess sensitivity, specificity and interrater reliability of phase-contrast MRI (PC-MRI) for diagnosing carotid near-occlusion. PATIENTS AND METHODS: Prospective cross-sectional study conducted between 2018 and 2021. We included participants with suspected 50%-100% carotid stenosis on at least one side, all were examined with CT angiography (CTA) and PC-MRI and both ICAs were analyzed. Degree of stenosis on CTA was the reference test. PC-MRI-based blood flow rates in extracranial ICA and intracranial cerebral arteries were assessed. ICA-cerebral blood flow (CBF) ratio was defined as ICA divided by sum of both ICAs and Basilar artery. RESULTS: We included 136 participants. The ICAs were 102 < 50% stenosis, 88 conventional ⩾50% stenosis (31 with ⩾70%), 49 near-occlusion, 12 occlusions, 20 unclear cause of small distal ICA on CTA and one excluded. For separation of near-occlusion and conventional stenoses, ICA flow rate and ICA-CBF ratio had the highest area under the curve (AUC; 0.98-0.99) for near-occlusion. ICA-CBF ratio ⩽0.225 was 90% (45/49) sensitive and 99% (188/190) specific for near-occlusion. Inter-rater reliability for this threshold was excellent (kappa 0.98). Specificity was 94% (29/31) for cases with ⩾70% stenosis. PC-MRI had modest performance for separating <50% and conventional ⩾50% stenosis (highest AUC 0.74), and eight (16%) of near-occlusions were not distinguishable from occlusion (no visible flow). CONCLUSION: ICA-CBF ratio ⩽0.225 on PC-MRI is an accurate and reliable method to separate conventional ⩾50% stenosis and near-occlusion that is feasible for routine use. PC-MRI should be considered further as a potential standard method for near-occlusion detection, to be used side-by-side with established modalities as PC-MRI cannot separate other degrees of stenosis.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".