Can mechanical myotonometry or electromyography be used for the prediction of intramuscular pressure?
Bibliographic record
Abstract
The aim of the study was to characterize the electromechanical properties of skeletal muscle during isometric loading as well as to assess the potential of estimating intramuscular pressure by electrical and mechanical methods. Simultaneous electromyography (EMG), mechanical myotonometry (MYO, frequency and decrement of decay) and intramuscular pressure (IMP) measurements were conducted at rest and during short-term and long-term isometric contractions in patients with chronic pain in the anterior leg or dorsal forearm. The EMG amplitude and MYO(freq) accounted significantly (24-73%, p < 0.0001) for the variations in the IMP under short-term isometric loading. The IMP, EMG and MYO(freq) increased linearly with the relative muscle load (r = 0.868-0.993, p < 0.05). Mean values of EMG amplitudes at the contraction levels of 75% and 100% maximum voluntary contraction (MVC) and MYO(freq) values at all contraction levels (0-100% MVC) were higher for subjects with pathological values of IMP than for those with IMP values in the normal range. Total changes in IMP and EMG amplitude during 1 min isometric contraction were linearly interrelated (r = 0.747, p < 0.0001). We conclude that both surface electromyography and myotonometry parameters are indicative of intramuscular pressure, but neither of these methods can be used alone to diagnose non-invasively chronic compartment syndrome with acceptable accuracy.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".