Attributing Hypodensities on CT to Angiographic Vasospasm Is Not Sensitive and Unreliable
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The presence of low-density areas on CT is used in clinical decision-making regarding treatment of angiographic vasospasm as well as in research as a surrogate marker for severity of angiographic vasospasm. We assess the interobserver variability in attributing hypodensities on CT to angiographic vasospasm-related delayed ischemic neurological deficit. METHODS: Three experienced reviewers, 2 neurosurgeons, and a neuroradiologist independently reviewed CT scans of 413 patients enrolled in the Clazosentan to Overcome Neurological iSChemia and Infarction OccUrring after Subarachnoid hemorrhage (CONSCIOUS-1) trial, who universally underwent catheter angiography to determine severity of angiographic vasospasm. Interobserver variability was calculated using the κ statistic and the χ(2) test was used to determine associations between dichotomized outcomes. RESULTS: There was considerable interobserver variability in attributing CT hypodensities to vasospasm-related delayed ischemic neurological deficit (κ=0.51-0.78; 95% CI, 0.35-0.90). Patients with hypodensities attributed to delayed ischemic neurological deficit were significantly more likely to have severe angiographic vasospasm (P=0.001), but a substantial proportion of these patients (19%) also had mild or no spasm. CT hypodensities had a sensitivity and specificity of 41% and 93%, respectively, in identifying patients with severe angiographic vasospasm, even with expert consensus that these represent angiographic vasospasm-related delayed ischemic neurological deficit. CONCLUSIONS: We find considerable interobserver variability in attributing CT hypodensities to angiographic vasospasm and propose that they may not be a robust marker of severity of angiographic vasospasm, even with unanimous expert agreement that they are a result of vasospasm-related delayed ischemic neurological deficit. CLINICAL TRIAL REGISTRATION: URL: www.clinicaltrials.gov. Unique identifier: NCT00111085.
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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 | 0.031 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 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".