Response to Letter by Bladin et al
Bibliographic record
Abstract
Measurement of Carotid Arteries to Quantify Carotid Stenosis" 1 and our related works.[2][3][4] We agree that the optimal method to quantify carotid stenosis remains somewhat controversial.All methods of carotid stenosis quantification are relatively flawed, despite the imaging modality or the statistical technique.Nonetheless, the attempts to improve this quantification have all played an important role in our understanding of carotid disease and measurement methodology.Bladen et al correctly state our message that carotid stenosis should be directly measured and individualized.We agree that we could have measured the widest portion of the carotid bulb, instead of at the level of tightest luminal stenosis.Yet, the carotid stenosis index (CSI) 5 does not involve measurement of the carotid bulb at all.Instead the CSI method relied on a presumed "fixed anatomic relationship" 5 between the common carotid artery (CCA) and the carotid bulb to provide an estimation of the widest point of the carotid bulb.This "fixed anatomic relationship" is far from fixed, with other authors reporting standard deviations ranging from Ϯ0.09 to Ϯ0.19.6 The CSI authors also report that estimations of the carotid bulb via measurement of the CCA is more accurate, because the CCA is easier to measure, is disease free and has less anatomic variation.5 With the high quality data from CTA, all vessels can be viewed and measured with the same ease, atherosclerotic disease can be identified (with qualification of plaque content), and anatomic variation is easier to identify because one CTA examination provides data for the entire neck vasculature.CTA gives high resolution imaging of all arteries as well as the soft tissue details of the arterial wall.4 The outside arterial wall can be identified consistently, despite claims to the otherwise.The wall is well demarcated from surrounding peri-arterial fat, and from different densities of intraluminal plaque.There are technical choices that need to be considered to properly evaluate the arteries as we have described.[1][2][3][4] Specifically, this involves the rewindowing of images using a digital PACS system, rather than interpretation from filmed images.For now, carotid quantification methods will most likely remain individualized by regional and individual healthcare centers and physicians, depending on the mix of available technologies, resources and local expertise.One of the goals of our work was to introduce CTA as yet another method to quantify carotid stenosis.CTA has become the preferred angiographic modality at our center, and many others, because of its lack of stroke risk, ability to directly measure in millimeters, ease of standardization of CTA, the quickness of the examination (seconds to acquire images from the aortic arch to vertex), low demand of labor-intensive resources, and the high quality data produced.Catheter angiography is no longer the "gold standard" in identifying carotid stenosis.Current CTA techniques allow for direct quantification and visualization of the neck vasculature from the arch through vertex in only a few seconds.CTA technology is readily available, can be performed by a single qualified technologist, and provides high quality data that was previously only available through catheter angiography, however, without risk of stroke.
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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 | 0.004 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.085 | 0.045 |
| Insufficient payload (model declined to judge) | 0.013 | 0.016 |
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".