Tolerance and Physiological Correlates of Neuromuscular Electrical Stimulation in COPD: A Pilot Study
Bibliographic record
Abstract
RATIONALE: Neuromuscular electrical stimulation (NMES) of the lower limbs is an emerging training strategy in patients with COPD. The efficacy of this technique is related to the intensity of the stimulation that is applied during the training sessions. However, little is known about tolerance to stimulation current intensity and physiological factors that could determine it. Our goal was to find potential physiological predictors of the tolerance to increasing NMES stimulation intensity in patients with mild to severe COPD. METHODS: 20 patients with COPD (FEV1 = 54±14% pred.) completed 2 supervised NMES sessions followed by 5 self-directed sessions at home and one final supervised session. NMES was applied simultaneously to both quadriceps for 45 minutes, at a stimulation frequency of 50 Hz. Spirometry, body composition, muscle function and aerobic capacity were assessed at baseline. Cardiorespiratory responses, leg discomfort, muscle fatigue and markers of systemic inflammation were assessed during or after the last NMES session. Tolerance to NMES was quantified as the increase in current intensity from the initial to the final NMES session (ΔInt). RESULTS: Mean ΔInt was 12±10 mA. FEV1, fat-free-mass, quadriceps strength, aerobic capacity and leg discomfort during the last NMES session positively correlated with ΔInt (r = 0.42 to 0.64, all p≤0.06) while post/pre NMES IL-6 ratio negatively correlated with ΔInt (r = -0.57, p = 0.001). FEV1, leg discomfort during last NMES session and post/pre IL-6 ratio to NMES were independent factors of variance in ΔInt (r2 = 0.72, p = 0.001). CONCLUSION: Lower tolerance to NMES was associated with increasing airflow obstruction, low tolerance to leg discomfort during NMES and the magnitude of the IL-6 response after NMES. TRIAL REGISTRATION: ClinicalTrials.gov NCT00809120.
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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 | 0.001 |
| 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 | 0.001 | 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".