Validation of the Comprehensive Hierarchical Evaluation of Disability based on Activity Limitations (miFunction) Scale: A Pilot Study
Bibliographic record
Abstract
Background: A reliable measure of deficits following stroke is crucial in the measurement of the outcome of a therapeutic stroke trial. The modified Rankin Scale (mRS) is currently the most widely used primary outcome measure in acute stroke trials despite substantial interobserver variability, which impairs outcome assessment and reduces the power of clinical trials. In 2000, the World Health Organization (WHO) developed a patient-centered, International Classification of Functioning, Disability and Health (ICF). Guided by the ICF, stroke researchers and clinicians at the University of Calgary developed a comprehensive, hierarchical assessment tool (miFunction) to address the shortcomings of the mRS and deliver a more thorough understanding of disability following stroke as it is now defined. Methods: The initial validation of miFunction involved an assessment of inter-observer reliability (Kappa Statistic) as well as its convergent validation (Pearson Correlation Coefficient) against the mRS. Participants were recruited from a population of stroke survivors at an outpatient stroke prevention clinic in Calgary, Alberta and were eligible for inclusion to the study if they had been diagnosed with stroke within 60 days. A sample size of 30 was the target and was achieved over the course of 5 months in 2013 Results: Although the results demonstrated only moderate inter-observer agreement (k= 0.585, p<.005), almost perfect correlation between miFunction and mRS was established (r=0.821, p<0.05). Discussion: miFunction has demonstrated a strong ability to assess disability following stroke, though there remains work to be done to improve its inter-observer reliability. This work must further consider the language used for the assessment questions so as to avoid ambiguity, confusion or misinterpretation.
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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 | 0.019 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".