P.067 The decision to revascularize in symptomatic non-stenotic carotid disease: results from the Hot Carotid Qualitative study
Bibliographic record
Abstract
Background: Little evidence exists to guide the management of symptomatic non-stenotic carotid disease (SyNC). SyNC, which refers to carotid lesions with less than 50% artery stenosis, has been increasingly implicated as a cause of stroke and TIA. Methods: Semi-structured interviews with 22 stroke physicians from 16 centers were conducted as part of the Hot Carotid Qualitative Study. This study explored decision-making approaches, opinions and attitudes regarding the management of symptomatic carotid disease. Presented here are a subset of results related to the decision to revascularize patients with SyNC. Results: Thematic analysis revealed equipoise in the decision to revascularize patients with SyNC. Participants discussed a desire to use imaging features (e.g plaque rupture and plaque morphology) to inform the decision to revascularize, though significant uncertainty remains in appraising the risk conferred by certain features. Experts support further study to better understand the use of these features in risk appraisal for patients with SyNC. Conclusions: The decision to revascularize patients with SyNC is an area with significant equipoise. Experts identify the use of imaging features as an important tool in informing the decision to pursue revascularization in patients with SyNC though more study is required in this area to better inform practice.
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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 | 0.028 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".