Abstract 3701: A Novel Web based modified Rankin Scale program for certification and patient self assessment scoring in the Antihypertensive Treatment in Acute Cerebral Hemorrhage (ATACH II) Trial
Bibliographic record
Abstract
Background: The modified Rankin Scale (mRS) is a commonly used disability scale in clinical trials pertaining to acute stroke. Previous studies suggest that requiring the use of certification programs can improve the accuracy of mRS grading. We developed two mRS video tools for use in the multinational ATACH II trial. These tools train medical professionals on assessing mRS and enable patients, or their caregivers, to ascertain their own mRS score. Methods: After conducting a thorough literature review and interviewing numerous accomplished neurologists, we focused the development of our mRS resources around two concepts: key differentiating points and standard interview questions. The certification program consists of two elements: an educational video and an assessment. The educational video describes the mRS scale in four formats: Power Point slides with narrator explanation, simulated clinical interviews, videos of patients outside of the clinical setting, and narrator overview of specific levels. After watching this video, users proceed to the assessment where they are asked to assess the mRS score of six patients based on visual assessment and specific answers to the six foundation interview questions. Users are required to assess all six scenarios correctly in order to pass. Results: To date 61 individuals have taken the certification course and test. 54 passed (88.5%) and 7 failed (11.4%) the test. The pass rate was very high in physicians and study coordinators (95.7% and 80.6%). Conclusion: A novel web based tool for mRS certification is described. We are currently designing validation studies for each of these tools. Pilot study is undergoing to validate the patient self assessment grading.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".