A comparison of 3 radiographic scales for the prediction of delayed ischemia and prognosis following subarachnoid hemorrhage
Bibliographic record
Abstract
OBJECT: Delayed cerebral ischemia is a major cause of morbidity and death following aneurysmal subarachnoid hemorrhage and requires timely intervention for a successful outcome to be achieved. In this study the investigators compared the commonly used Fisher scale with 2 newer radiographic scales for the prediction of vasospasm, delayed infarction, and poor outcome. METHODS: This was a single-center, retrospective cohort study involving 271 consecutive patients with a ruptured cerebral aneurysm. Without knowledge of subsequent events, admission CT scans were each assigned scores by using 3 different grading schemes: the Fisher, modified Fisher, and Claassen scales. For each of the scales, the relationship between an increasing score and the risk of later complications was assessed in univariate and multiple logistic regression analyses. RESULTS: With the Fisher scale, the risk of complications was relatively high when the score was 3, but not for other scores. In contrast, using the other scales, there was a more linear relationship between a rising score and the frequency of complications. This was particularly true for the modified Fisher scale, in which each stepwise increase was associated with an escalating risk of vasospasm, delayed infarction, and poor prognosis. Kappa scores measuring interobserver variability among 4 CT readers were also slightly better with the newer scales. CONCLUSIONS: Although the modified Fisher and Claassen scales have yet to be prospectively validated, the authors' findings suggest that the clinical performance of these systems is superior to that of the Fisher scale.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".