Manual Muscle Testing: A Method of Measuring Extremity Muscle Strength Applied to Critically Ill Patients
Bibliographic record
Abstract
Survivors of acute respiratory distress syndrome (ARDS) and other causes of critical illness often have generalized weakness, reduced exercise tolerance, and persistent nerve and muscle impairments after hospital discharge. Using an explicit protocol with a structured approach to training and quality assurance of research staff, manual muscle testing (MMT) is a highly reliable method for assessing strength, using a standardized clinical examination, for patients following ARDS, and can be completed with mechanically ventilated patients who can tolerate sitting upright in bed and are able to follow two-step commands. (7, 8) This video demonstrates a protocol for MMT, which has been taught to ≥ 43 research staff who have performed >800 assessments on >280 ARDS survivors. Modifications for the bedridden patient are included. Each muscle is tested with specific techniques for positioning, stabilization, resistance, and palpation for each score of the 6-point ordinal Medical Research Council scale. Three upper and three lower extremity muscles are graded in this protocol: shoulder abduction, elbow flexion, wrist extension, hip flexion, knee extension, and ankle dorsiflexion. These muscles were chosen based on the standard approach for evaluating patients for ICU-acquired weakness used in prior publications. (1,2).
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".