Standardized Nomenclature for Modified Rankin Scale Global Disability Outcomes: Consensus Recommendations From Stroke Therapy Academic Industry Roundtable XI
Bibliographic record
Abstract
The modified Rankin Scale (mRS), a 7-level, clinician-reported, measure of global disability, is the most widely employed outcome scale in acute stroke trials. The scale's original development preceded the advent of modern clinimetrics, but substantial subsequent work has been performed to enable the mRS to meet robust contemporary scale standards. Prior research and consensus recommendations have focused on modernizing 2 aspects of the mRS: operationalized assignment of scale scores and statistical analysis of scale distributions. Another important characteristic of the mRS still requiring elaboration and specification to contemporary clinimetric standards is the Naming of scale outcomes. Recent clinical trials have used a bewildering variety, often mutually contradictory, of rubrics to describe scale states. Understanding of the meaning of mRS outcomes by clinicians, patients, and other clinical trial stakeholders would be greatly enhanced by use of a harmonized, uniform set of labels for the distinctive mRS outcomes that would be used consistently across trials. This statement advances such recommended rubrics, developed by the Stroke Therapy Academic Industry Roundtable collaboration using an iterative, mixed-methods process. Specific guidance is provided for health state terms (eg, Symptomatic but Nondisabled for mRS score 1; requires constant care for mRS score 5) and valence terms (eg, excellent for mRS score 1; very poor for mRS score 5) to employ for 23 distinct numeric mRS outcomes, including: all individual 7 mRS levels; all 12 positive and negative dichotomized mRS ranges, positive and negative sliding dichotomies; and utility-weighted analysis of the mRS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.112 | 0.201 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.014 | 0.008 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.015 | 0.022 |
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".