Assessments of arterial and venous phase radiodensity does not improve carotid near-occlusion diagnostics
Bibliographic record
Abstract
The hypothesis of this study was that evaluation of radiodensity assessment beyond a carotid stenosis in arterial and/or venous phase can be used to separate near-occlusion and conventional ≥ 50% stenosis. We prospectively included participants with ≥ 50% carotid stenosis with inclusion preference for cases with extracranial internal carotid artery (ICA) asymmetry. All participants were examined with a research biphasic computed tomography angiography (CTA) protocol (arterial and venous phase). Reference diagnosis was set by interpretation on CTA and radiodensity difference between ipsilateral and contralateral ICA (c-corrected) or vertebral (v-corrected) was compared. We included 93 participants, 62 with near-occlusion and 31 with conventional ≥ 50% stenosis. Just beyond the stenosis, median c-corrected radiodensity was - 20 Hounsfield units (HU) among near-occlusions and - 1 HU among conventional ≥ 50% stenoses (p < 0.001) in the arterial phase. For the venous phase, these findings were + 17 HU and + 3 HU (p = 0.007). Similar group differences were seen for v-correction. No parameter had good diagnostic performance, area under the curve ≤ 0.82. With specificity set at ≥ 95%, detected near-occlusions were foremost those with large side-to-side differences in distal ICA-diameter. Carotid near-occlusions can have reduced radiodensity beyond the stenosis in arterial phases and increased radiodensity in venous phases compared to a reference artery-which was not clearly seen for conventional stenoses. However, these radiodensity findings are best seen in near-occlusion cases that are not diagnostically challenging, while they work poorly as additional diagnostic aids.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".