Opinions about the use of the recognition of stroke in the emergency room scale
Bibliographic record
Abstract
Sir, We have recently published an article about the use of the recognition of stroke in the emergency room (ROSIER) scale in pre-hospital assessment of stroke in Annals of Indian Academy of Neurology.[1] And it was cited in the Canadian Best Practice Recommendations for Stroke Care 2013 (Fourth Edition) at last week.[2] Please allow us to share a few opinions about the use of the ROSIER scale. In the new Canadian stroke care recommendations guidelines, the ROSIER scale was introduced in detail. And the related published studies about the use of the ROSIER scale were reported. Especially, the new Canadian stroke care guidelines mentioned our study like this: The ROSIER scale was not developed for pre-hospital assessment, but rather was designed for use in the identification for probable stroke by emergency room (ER) physicians. It has been evaluated for use in a pre-hospital setting only once in a limited setting in China where sensitivity was reported to be 90% and specificity 83%.[2] Who can use the ROSIER scale accurately? In previous study, we validated the ROSIER scale could be used by ER physicians in the pre-hospital setting. And in another study, we also recommended ER physicians to use the ROSIER scale both in the pre-hospital setting and in the ER.[3] However, the small size and single center setting limited the study. In my opinion, the ER physicians must get a series of stroke recognition and treatment training and master the initial stroke assessment and stabilization in the ER. Until now, there is not a recognized “paramedics” profession in China. Hence, we are not sure whether the ROSIER scale could be used by paramedics. As we know, Byrne B and O’Halloran P reported that registered nurses working on a stroke unit using the ROSIER assessment tool are able to diagnose stroke with a degree of accuracy comparable to doctors using clinical neurological assessment. And they achieved a diagnostic sensitivity for stroke of 98% (95% confidence interval 88-99), positive predictive value 83% (95% confidence interval 73-90).[4] However, some nursing experts didn't support them.[5] Whether the ROSIER scale could be used by ER physicians in the pre-hospital setting or by registered nurses in the ER is still a problem. Because we lack of convincing evidence and need large size and multi-center validation. Whoever using the ROSIER scale, the common target is to reduce the delay of early assessment and ineffective triage on suspected stroke patients and increase the chance of administration of thrombolytic therapy. The search for an ideal stroke recognition tool must continue. There is still a long way to go.
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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 | 0.030 | 0.219 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.012 | 0.020 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".