The feasibility and acceptability of neuromuscular electrical stimulation to improve exercise performance in patients with advanced cancer: a pilot study
Bibliographic record
Abstract
BACKGROUND: To determine the feasibility and acceptability of lower limb neuromuscular electrical stimulation (NMES) as a home-based exercise therapy in patients with cancer who could not attend hospital-based exercise training. METHODS: A single-arm prospective pilot study of NMES, applied daily to both quadriceps muscles for six weeks. Participants were recruited from patients referred to a hospital-based multi-disciplinary supportive care team specializing in treatment of patients with nutritional depletion and functional decline. RESULTS: Of the 15 participants who underwent baseline testing, 10 (67%) completed the study and only one (7%) withdrew because of discomfort due to NMES treatment. 7/10 (70%) of participants used NMES at least three times a week for the duration of the study. Use of NMES did not lead to significant improvements in physical performance tests. CONCLUSIONS: NMES is a feasible and acceptable intervention for home use in patients with cancer, poor performance status and metastatic disease. However, whether NMES is an effective strategy to stabilize or improve physical performance in such patients is not proven.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".