Rlative shoulder fexor and handgrip strength is related to upper limb function after stroke
Bibliographic record
Abstract
OBJECTIVE: To compare the relative strength of different muscle groups of the paretic upper limb and assess the relationship with motor performance. DESIGN: Descriptive study. SETTING: Secondary care rehabilitation centre. SUBJECTS: A convenience sample of 13 chronic hemiparetic stroke subjects. MAIN OUTCOME MEASURES: The maximal active torques of five muscle groups were measured in both upper limbs (UL) and converted into relative strength (paretic/nonparetic). The UL function was assessed using the Box and Block Test, the Finger-to-Nose Test, the Fugl-Meyer Test and the TEMPA (Test Evaluant les Membres supérieurs des Personnes Agées). RESULTS: The Friedman two-way analysis of variance shows a significant difference across the relative strength of the different muscle groups (p = 0.017), but subsequent multiple comparisons indicate a significant difference between handgrip and elbow extension only (relative strength of 0.52 +/- 0.27 and 0.73 +/- 0.23 respectively). However, data show the presence of large intrasubject imbalances between muscle groups. The relative forces for shoulder flexion and handgrip are the best predictors of the UL function, the higher Spearman's rho correlation coefficients for each clinical test ranging from 0.70 to 0.81. CONCLUSIONS: These results do not confirm classical clinical teaching regarding the distribution of weakness following stroke (e.g., proximal to distal gradient; extensors more affected than flexors) but support the hypothesis that strength is related to the function of the paretic upper limb.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".