Early abnormal transient hyperemic response test can predict delayed ischemic neurologic deficit in subarachnoid hemorrhage
Bibliographic record
Abstract
BACKGROUND: Early detection of vasospasm is crucial to prevent significant delayed ischemic neurological deficit post subarachnoid hemorrhage. The standard methods of detection, including cerebral angiogram and computed tomography are invasive and not safe to be repeated, as is very often indicated clinically. Transient hyperemic response test has been previously used to predict autoregulation failure in traumatic brain injury and subarachnoid hemorrhage. AIMS: We investigate the usability of transient hyperemic response test as a predictor of clinical vasospasm in a cohort of patients with aneurismal subarachnoid hemorrhage. METHODS: A retrospective review of all THRT examinations done between January 2011 and July 2012 conducted at Montreal Neurological Institute and Hospital and the Montreal General Hospital. Patients diagnosed with aSAH in which the THRT was performed within the first 24-48 h of admission were included in the study. Two-dimensional transcranial Doppler images were obtained and velocities were recorded. A positive response was one in which the velocity was increased by more than 9% of the baseline systolic velocity, indicating an intact cerebral autoregulation. Lindegaard ratio > 3 is considered abnormal and in the context of elevated systolic velocity of the MCA, is highly suggestive of DIND. RESULTS: Fifteen patients met the inclusion criteria. A total of 6 patients developed clinical and radiological vasospasm. Out of these 6 patients, 5 (83%) had an abnormal THRT in the initial TCD assessment (p = 0.0406). We found that abnormal transient hyperemic response test readings are predictive of subsequent vasospasm development. CONCLUSIONS: The results of this small retrospective study support the notion that transient hyperemic response test has predictive value in vasospasm development and may prove useful in patient monitoring and successful clinical management.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".