Abstract TP375: Screening for Nontraditional Stroke Symptoms in Women: What is the Evidence? A Systematic Review
Bibliographic record
Abstract
Background and Purpose: Stroke is the fifth leading cause of death in the United States. It is projected by the year 2030, there will be a 20.5% increase in the prevalence of stroke, with women being at highest risk. Women can present with nontraditional symptoms of stroke. The purpose of this review of literature was to determine the accuracy, reliability, validity, and sensitivity of existing stroke screening tools that incorporate nontraditional symptoms, and examine gender differences. Methods: A systematic review of peer-reviewed literature was completed in five databases: CINAHL Google Scholar, ProQuest, PubMed, and SCOPUS. Websites were scanned for gray material. Key terms where utilized as text words, title, abstract and medical subject headings to identify related studies of relevance. Results: References were screened by title and abstract. Twenty-nine out of 1175 articles met the inclusion criteria. Seven stroke screening tools were identified; Melbourne Ambulance Stroke Screen, Los Angeles Prehospital Stroke Screen (LAPSS), Cincinnati Prehospital Stroke Scale, Ontario Prehospital Stroke Screening Tool, Medic Prehospital Assessment for Code Stroke, the Recognition of Stroke in the Emergency Room, and Face Arm Speech Test. A thematic synthesis approach organizing data according to patterns identified: early recognition of stroke is crucial to timely treatment and better patient outcomes, a difference in stroke symptoms exists between genders, knowledge and awareness of nontraditional stroke symptoms are essential for early identification, current stroke screen tools have a potential for nearly 30% error of non-identification of stroke, and only 2 of 7 screening tools identify more than 1 of 11 non-traditional symptoms with the most encouraging tool (LAPSS) only identifying 3 symptoms (loss of consciousness, confusion, and seizures). Conclusions: Evidence suggests limitations exists within current stroke screens. Poor recognition of nontraditional symptoms due to lack of stroke screening tools may delay treatment and worsen patient outcomes. Research is required to identify variables and develop a stroke screening tool that is sensitive and specific to nontraditional stroke symptoms in women.
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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 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.008 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".