The Effect of Training and Certification for the NIHSS and the mRS on Rater Performance: A Systematic Review
Bibliographic record
Abstract
ABSTRACT Background: Up-to-date certification of the National Institutes of Health Stroke Scale (NIHSS) and modified Rankin Scale (mRS) is often required for clinical trials, representing a significant burden on clinical investigators globally. Aims: This systematic review sought to determine if NIHSS or mRS training, re-training, certification or recertification led to improvements in the reliability or accuracy of ratings as well as other relevant user metrics (e.g., user confidence). Results: Among 4227 studies, 100 passed screening and were assessed for eligibility with full-text review; 23 met inclusion criteria. Among these 23 studies, 22 examined NIHSS training and/or certification, and only a single study included examined the effect of training on mRS performance. Ten of 23 included studies were conference abstracts. The study designs, interventions and outcome measurement of the included studies were heterogeneous. In the case of the NIHSS, two studies found increased accuracy after NIHSS training, and a third study showed statistically significant though clinically trivial decreases in error rate with training. The remaining 19 studies showed no benefit of NIHSS training as it relates to reliability or accuracy outcomes. The single included mRS study did not show the benefit of training. Conclusion: Although data are sparse with heterogeneous training protocols and outcomes, there is no compelling evidence to suggest benefit of healthcare professionals completing NIHSS or mRS training, certification or recertification. At the very least, recertification/re-training requirements should be reconsidered pending the provision of robust evidence.
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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 | 0.018 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".