Derivation of Power M‐Mode Transcranial Doppler Criteria for Angiographic Proven MCA Occlusion
Bibliographic record
Abstract
BACKGROUND: Stringent transcranial Doppler (TCD) criteria for diagnosing occlusion are needed for more reliable TCD performance at bedside in the acute stroke setting. SUBJECTS AND METHODS: At three academic stroke centers, we performed TCD examination for patients with symptoms of cerebral ischemia who underwent digital subtraction angiography (DSA). We used a standard insonation protocol with power M-mode Doppler (PMD) TCD (TCD 100 M, Spencer Technologies Inc., Seattle, WA). We collected mean flow velocity (MFV), pulsatility indices (PI), and power M-mode resistance signature (absent, high, or low) in symptomatic middle (MCA), anterior (ACA), posterior (PCA), and in affected (a), ipsilateral (i), and contralateral (c-lat) cerebral arteries. Ratios of aMCA/c-lat MCA, aMCA/iACA, and aMCA/iPCA MFV were subsequently calculated. PMD-TCD flow findings were evaluated with a receiver-operating characteristic (ROC) analysis for angiographically proven MCA occlusion. RESULTS: We studied 120 patients with acute cerebral ischemia with PMD-TCD examinations prior to or immediately after DSA. Lower aMCA velocities pointed to higher probability of occlusion (P= .055). The aMCA/iPCA MFV ratio was superior to the aMCA/iACA ratio and strongly predictive of occlusion at a threshold ratio of 0.5 (RR 2.31 CI(95) 2.13-2.51). High resistance or absent M-mode flow signatures in the proximal MCA were present in 87% of M1 and M2 MCA occlusions (probability 87%). In the presence of a low-resistance PMD signature, obtaining the aMCA/iPCA MFV ratio <0.5 increases probability of occlusion to 87%. Normal MFV ratios and low-resistance M-mode signatures are highly predictive of a negative angiogram for MCA occlusion. CONCLUSION: In acute cerebral ischemia, reliable criteria for proximal MCA occlusion have been developed based on combination of MFV ratios and M-mode flow resistance signatures. Validation of these criteria will require multicenter studies.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".