Comprehensive assessment of disability post-stroke using the newly developed miFUNCTION scale
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The modified Rankin Scale (mRS) is the most widely used primary outcome measure in acute stroke trials. However, substantial interobserver variability impairs outcome assessment as well as reduces power of clinical trials. Guided by the International Classification of Functioning, Disability and Health, we developed a comprehensive, hierarchical assessment tool (miFUNCTION) to address the shortcomings of the modified Rankin Scale and deliver a more thorough understanding of disability following stroke. METHODS: The initial construct validity of miFUNCTION was established in a pilot study of patients at an outpatient stroke prevention clinic that had been diagnosed with stroke within 60 days. To further assess criterion validity, miFUNCTION was compared against the modified Rankin Scale and other outcome measures within the Endovascular Treatment for Small Core and Anterior Circulation Proximal Occlusion With Emphasis on Minimizing CT to Recanalization Times (ESCAPE) trial. Logistic regression analysis with miFUNCTION as an outcome was used to demonstrate the beneficial effect of endovascular treatment. RESULTS: The pilot study showed moderate inter-observer agreement (k = 0.585, p < 0.005) but near perfect correlation between miFUNCTION and modified Rankin Scale (ρ = 0.821, p < 0.05). The correlation of miFUNCTION and modified Rankin Scale was near perfect again in the ESCAPE trial (ρ = 0.944). Effect size of the multivariable models using modified Rankin Scale (adjusted odds ratio: 3.45, 95% confidence interval: 2.05-5.78) and miFUNCTION (adjusted odds ratio: 3.32, 95% confidence interval: 1.99-5.55) as an outcome measure for the ESCAPE trial patients was similar. CONCLUSIONS: miFUNCTION is strongly associated with the degree of disability following stroke both in an outpatient setting and a clinical trial. Further work remains to assess sensitivity to change and to improve the inter-observer reliability of the scale.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".