A comparison of a standard neurological assessment tool to a stroke scale for detecting symptomatic cerebral vasospasm
Bibliographic record
Abstract
One of the primary causes of disability and death in individuals who have experienced a subarachnoid hemorrhage due to an aneurysm rupture is cerebral vasospasm. Vasospasm can cause a general decrease in the level of consciousness or the onset of focal deficit such as hemiplegia or aphasia. Early detection of vasospasm is critical in allowing prompt intervention and treatment to prevent further ischemia or infarction. The nurses role in observing and detecting changes in these critically ill patients was guided by the Nursing Model of Hospitalization Events (Smith, 1998). The research study consisted of comparing two assessment tools for quantitative data analysis of early detection of symptomatic vasospasm. The standard neurological record that is currently used was compared to the stroke scale developed by the National Institute of Neurological Disorders, and Stroke, and the National Institute of Health. The methodology was also comprised of a qualitative component using content analysis of the nurses' notes to enhance information regarding the patients' neurological status. There was no statistical significance demonstrated between the vasospasm and non-vasopasm groups, however several clinically relevant findings were shown. In particular the assessment of focal symptoms such as motor power will be discussed. Observations by the nurses regarding generalized changes in neurological status revealed findings such as restlessness, impulsiveness, and unusual behaviors are highlighted and provide evidence for future investigation. All findings and their relevance to the nurse's role in detecting symptomatic vasospasm will be discussed.
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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 | 0.014 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".