Letter
Bibliographic record
Abstract
To the Editor: We read the article by Fanous et al1 with interest, and we applaud the authors for their careful analysis of the preprocedural factors associated with greater risk during carotid artery stenting (CAS). We particularly noted the authors' concerns regarding concentric plaque calcification and anatomical features that may be “hostile” to the use of distal protection devices. Since 2000, we have tended away from a habitual approach to the technical steps taken during CAS, particularly regarding the use of pre- and poststent balloon angioplasty and the use of distal protection devices.2 Our simple approach, which we have termed primary carotid stenting, eschews the routine use of post- and prestent balloons and has been demonstrated by us3 and others4,5 to be successful in the majority of symptomatic, severely stenotic plaques. We also note the trend of other groups,6,7 including the authors,8 away from a “one technique fits all” approach to CAS. We agree with the authors' emphasis on plaque calcification. We have found it helpful to use a carotid plaque morphology-based scale (the “PLAC” scale) based on radiographic factors obtained during preprocedural computed tomographic angiography.9 We found that the degree of plaque calcification (as the authors also assert) and, moreover, the presence or absence of moderate “soft” plaque and the thickness of the calcification itself are the 3 independent risk factors associated with long-term successful outcome. We believe these factors may complement those found useful by the authors during the preprocedural assessment of symptomatic carotid stenosis patients. Disclosure The authors have no personal, financial, or institutional interest in any of the drugs, materials, or devices described in this article.
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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 | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.064 | 0.037 |
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".