Improving lower limb function in patients with major muscular loss or denervation following resection of a sarcoma
Bibliographic record
Abstract
[Truncated abstract] Lower limb soft tissue sarcomas are rare malignant tumours that often occupy large portions of muscular tissue, peripheral nerves or bone. Major muscle and peripheral nerve resections are now less likely to result in limb amputation due to the use of neoadjuvant and/or adjuvant therapy, and the ability to obtain a wide surgical margin, allowing limb salvage surgery (LSS) to be the preferred treatment modality. However, LSS may still leave patients with functional disabilities which may cause persistent emotional and social suffering. Numerous randomised studies have investigated the effects of exercise interventions in cancer survivors, revealing aerobic, resistance, and their combination to be safe, feasible and beneficial interventions. Currently, no researchers have investigated the effects of an exercise rehabilitation programme on the restoration of lower limb function following major muscle loss or denervation due to sarcoma resection; and no recommendations have been made for exercising patients with damage to their sciatic or femoral nerve. In this study we evaluated the effects of a 12 week supervised exercise programme on muscle strength, muscle mobility, functional movement, quality of life (QOL) and ability to perform activities of daily living. Nine patients (5 female, 4 male) with damage to their sciatic or femoral nerve, or respective areas of muscle innervation following LSS, participated in the study. Patients attended the clinic three times per week for 12 weeks of supervised exercise rehabilitation which comprised of aerobic, resistance and hydrotherapy training. Patients’ physical and mental status in the form of the Short Form 36 (SF-36), Toronto Extremity Salvage Score (TESS), High Level Mobility Assessment Tool (HiMAT), strength (isokinetic and/or 3RM), joint range of motion (ROM), six minute walk test (6MWT) and timed up and go (TUG) were collected twice at baseline, then at 6 and 12 weeks into the intervention.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".