Abstract TP252: Prehospital Stroke Scales as a Tool for Early Identification of Stroke and Transient Ischemic Attacks: A Cochrane Systematic Review
Bibliographic record
Abstract
Background: Stroke remains among the leading causes of death and disability worldwide. The recent advances in acute stroke therapy further validate the need for an efficient ‘stroke system,’ of which the first step is rapid and accurate identification of stroke. Currently, there is no agreed upon standard prehospital stroke scale. Methods: In conjunction with the Cochrane Stroke Group we developed an electronic search strategy to identify relevant studies in MEDLINE. We then adapted it to seven other databases. Scales had to have been used in the emergency or prehospital setting, have a discharge diagnosis of stroke by a neurologist and needed to demonstrate at a minimum, the data needed to construct a two by two table. Our database search yielded 8479 references with 16 full texts articles and 4 abstracts (7 scales) meeting our inclusion criteria. We applied the Quadas-2 tool to eleiminate bias. Results: The ROSIER scale demonstrated the highest median sensitivity at 90% (95%CI, range 85%-97%) as well as the smallest confidence intervals. The LAPSS demonstrated the best pooled specificity, 91% (95%CI, range 84%-95%). Conclusion: In the acute stroke setting a highly sensitive scale for stroke identification is of great value. This Cochrane Systematic review demonstrated that of all currently validated scales, the ROSIER scale has the greatest sensitivity for detecting stroke in the the prehospital or emergency setting.
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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 | 0.011 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".