Transcranial Doppler Velocities and Angiographic Vasospasm after SAH: A Diagnostic Accuracy Study
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: After aneurysmal SAH, transcranial Doppler is commonly used to monitor cerebral vasospasm. The diagnostic accuracy of transcranial Doppler flow velocity values in detecting angiographic vasospasm in patients requiring urgent endovascular intervention has not been established. MATERIALS AND METHODS: We performed a retrospective analysis of a consecutive series of patients with aneurysmal SAH who underwent transcranial Doppler (index test) within 24 hours of conventional angiography (reference test). The judgment of 33%, 50%, and 66% degree of vessel narrowing on angiography was independently established by multiple neuroendovascular clinicians. Vessel-specific per-segment and per-patient transcranial Doppler velocities were studied using receiver operating characteristic curves, the Youden index, and minimal acceptable sensitivity models. Optimal mean flow-velocity thresholds were explored to calculate sensitivity and specificity using a per-patient judgment of vasospasm of at least 50% angiographic narrowing in any large arterial segment except A1. RESULTS: = . 001), but per-segment exploratory analyses yielded unsound mean flow velocity thresholds. The Youden and minimal acceptable sensitivity models proposed mean flow velocity thresholds of approximately 160 cm/s for the anterior circulation and 80 cm/s for the posterior circulation in the per-patient diagnosis of angiographic vasospasm (≥50%), yielding a sensitivity of 80%-90% (95% CI, 0.77-0.96), but with a corresponding specificity of 50% (95% CI, 0.40-0.56). CONCLUSIONS: In this study, a threshold transcranial Doppler mean flow-velocity value that would accurately diagnose ≥50% angiographic vasospasm remained elusive.
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 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".