Neuromuscular adaptation to early post-operative, high-intensity, short resistance training of non-operated lower extremity in elderly patients: A randomized controlled trial
Bibliographic record
Abstract
OBJECTIVE: To evaluate the short-term effects of high-intensity, task-specific vs regular rehabilitation programme on neuromuscular activity and functional changes in elderly inpatients. DESIGN: Single-blind, randomized controlled trial. PATIENTS: Sixty-two patients, aged 74.1 (standard deviation (SD) 6.9) years, 12.8 (SD 3.6) days after operation of one lower limb. METHODS: Allocation to either high- or regular-intensity strength training of non-operated limb, of 3 weeks duration. OUTCOME MEASUREMENTS: ratio of integrated electromyography per weight lifted (mm vastus medialis and lateralis), isometric electromyography (EMG), maximal weight lifted expressed as 1 repetition maximum, torque in Nm and isometric maximal force of uninvolved leg, timed "up and go" test (TUG) and Western Ontario and McMaster Universities (WOMAC) questionnaire. RESULTS: Both programmes resulted in significant improvements in: integrated EMG/weight lifted ratio, isometric EMG, 1 repetition maximum, torque and isometric force, TUG performance and WOMAC items (all p < 0.0001, Wilcoxon signed-rank test). Significant differences were found between the high- and regular-intensity groups for integrated EMG/weight lifted (p = 0.026), 1 repetition maximum and torque (p = 0.0014, Wilcoxon rank-sum test). CONCLUSION: High-intensity, short-period, early postoperative muscle training has a superior effect on maximal weight lifted by the knee extensors and integrated EMG/weight lifted compared with regular raining. These findings are in accordance with more rapid neuronal adaptation by a high intensity rehabilitation programme.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".