Editorial Comment—Remote Evaluation of Acute Ischemic Stroke: A Reliable Tool to Extend Tissue Plasminogen Activator Use to Community and Rural Stroke Patients?
Bibliographic record
Abstract
Background and Purpose-Despite Food and Drug Administration approval of tissue-type plasminogen activator for stroke, obstacles in the US healthcare system prevent its widespread use.The Remote Evaluation for Acute Ischemic Stroke (REACH) program was developed to address these issues in rural settings.A key component of stroke assessment in the REACH system is the National Institutes of Health Stroke Scale (NIHSS) evaluation.We sought to determine whether, using the REACH system, NIHSS values of bedside and remote evaluators would correspond.Methods-Twenty patients were recruited.On obtaining consent, a neurologist performed a bedside NIHSS evaluation on each patient.Within 1 hour, using any broadband-connected workstation-either office or home personal computer and a landline phone to speak with the patient-a second neurologist remotely evaluated the patient through the REACH system.Paired t tests and Pearson correlation coefficients were used to examine NIHSS reliability performed bedside and remotely.Results-NIHSS ranged from 1 to 24.Correlations between bedside and remote locations (rϭ0.9552,Pϭ0.0001) were very strong, and t tests indicate that the means were not different.Conclusions-The NIHSS can be reliably performed over the REACH system.This supports our endeavor to bring stroke expertise to rural community hospitals.(Stroke.2003;34:e188-e192.
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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 | 0.010 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.034 | 0.028 |
| Insufficient payload (model declined to judge) | 0.008 | 0.009 |
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".