Effectiveness of Diagnostic Strategies in Suspected Delayed Cerebral Ischemia
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Delayed cerebral ischemia (DCI) is a serious complication after aneurysmal subarachnoid hemorrhage. If DCI is suspected clinically, imaging methods designed to detect angiographic vasospasm or regional hypoperfusion are often used before instituting therapy. Uncertainty in the strength of the relationship between imaged vasospasm or perfusion deficits and DCI-related outcomes raises the question of whether imaging to select patients for therapy improves outcomes in clinical DCI. METHODS: Decision analysis was performed using Markov models. Strategies were either to treat all patients immediately or to first undergo diagnostic testing by digital subtraction angiography or computed tomography angiography to assess for angiographic vasospasm, or computed tomography perfusion to assess for perfusion deficits. According to current practice guidelines, treatment consisted of induced hypertension. Outcomes were survival in terms of life-years and quality-adjusted life-years. RESULTS: When treatment was assumed to be ineffective in nonvasospasm patients, Treat All and digital subtraction angiography were equivalent strategies; when a moderate treatment effect was assumed in nonvasospasm patients, Treat All became the superior strategy. Treating all patients was also superior to selecting patients for treatment via computed tomography perfusion. One-way sensitivity analyses demonstrated that the models were robust; 2- and 3-way sensitivity analyses with variation of disease and treatment parameters reinforced dominance of the Treat All strategy. CONCLUSIONS: Imaging studies to test for the presence of angiographic vasospasm or perfusion deficits in patients with clinical DCI do not seem helpful in selecting which patients should undergo treatment and may not improve outcomes. Future directions include validating these results in prospective cohort 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 | 0.012 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".