Importance of the imaging modality in decision making about carotid endarterectomy
Bibliographic record
Abstract
OBJECTIVE: To determine the influence of all possible imaging strategies on the appropriateness ratings for carotid endarterectomy, because less accurate noninvasive techniques are replacing contrast angiography, which was used in the major efficacy trials. METHODS: An expert panel, using appropriateness methodology, rated 203 scenarios where endarterectomy might be performed. Each scenario was rated where internal carotid artery stenosis was determined using five different imaging sources: 1) conventional angiography, 2) ultrasound carotid Doppler only, 3) CT (CTA) or MR (MRA) angiography only, 4) concordant results from two noninvasive carotid imaging studies, and 5) discordant results from two noninvasive studies. The scenarios deemed appropriate by conventional angiography were identified. The effect of the other imaging modalities on these results was examined. RESULTS: Thirty-three scenarios were identified as being appropriate. Concordant imaging results had no effect on appropriateness ratings in symptomatic carotid artery disease when compared with conventional angiography. Single noninvasive imaging techniques were deemed appropriate for investigation only in the presence of severe symptomatic stenosis. In all other scenarios, single noninvasive imaging and discordant results reduced the appropriateness rating of scenarios to either uncertain benefit or inappropriateness. The single appropriate scenario for asymptomatic carotid artery stenosis was where severe stenosis was determined by concordant noninvasive imaging or by CTA or MRA alone. CONCLUSION: It is important to take into account both the clinical scenario and the imaging modalities utilized to determine the degree of internal carotid artery stenosis in the clinical decision making surrounding carotid endarterectomy.
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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 | 0.072 | 0.254 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".