Comparison of a Standard Neurological Tool with a Stroke Scale for Detecting Symptomatic Cerebral Vasospasm
Bibliographic record
Abstract
The purpose of this study was to critically analyze the effectiveness of two tools used by nurses to assess neurological status of individuals at risk of developing cerebral vasospasm following aneurysmal subarachnoid hemorrhage due to aneurysm rupture. Early detection of vasospasm provides an opportunity for prompt treatment so that further ischemia or infarction can be prevented. We hypothesized that the National Institutes of Health Stroke Scale would detect symptomatic vasospasm earlier than the standard neurological record currently used in the practice setting of a tertiary care teaching hospital. Thirty participants were entered into the study, and a differential diagnostic process identified 15 with symptomatic vasospasm. Quantitative prospective and retrospective analysis showed that there was no statistical difference between the two scales in early detection of vasospasm. This finding may partially be explained by the clinical similarities between the vasospasm and nonvasospasm groups and by the challenges experienced by nurses in administering the stroke scale. Clinically relevant observations suggested the stroke scale was more effective in the assessment of focal symptoms. Qualitative content analysis of nursing notes also provided insight into clinical findings not captured on either scale regarding generalized changes such as restlessness, impulsiveness, and unusual behavior. This study demonstrates the need to develop a more appropriate tool for early detection of vasospasm.
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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 | 0.019 | 0.113 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".