Relationships among Three Clinical Measures of Muscle Tone at the Elbows of Individuals after a Stroke
Bibliographic record
Abstract
Patterns of associations among three common clinical muscle tone measures were investigated to determine their degree, and patterns of agreement. Data contributing to selection of clinical measures of muscle tone and understanding factors contributing to occupational dysfunctions were also sought. Forty five persons who were admitted after stroke to two occupational therapy rehabilitation programs were randomly selected. Their affected elbow's resting position (EJP), resistance to passive extension (ERM) and the angle where resistance first appeared (EAR) were measured by one, then a second therapist who also measured voluntary muscle function. Correlations among the three measures were calculated for both administrations and among patient subgroups with statistical correction for multiple correlations. Statistically significant associations appeared between ERM and EAR and between EJP and EAR. Highest statistically significant associations appeared among subjects with poor upper extremity function and those with low muscle tone. Patterns of associations were similar for the first and second administrations at both centres, though patterns among subgroups differed between centres. Correlation patterns suggest that biomechanical factors may influence the joint's resting position (EJP) more than ERM and EAR. Measures may be used interchangeably only with selected patient subgroups, which should also be the basis of method selection.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".