Prevention of deep tissue injury through muscle contractions induced by intermittent electrical stimulation after spinal cord injury in pigs
Bibliographic record
Abstract
Deep tissue injury (DTI) is a severe medical complication that commonly affects those with spinal cord injury. It is caused by prolonged external loading of the muscles, entrapping them between a bony prominence and the support surface. The entrapment causes excessive mechanical deformation and increases in interstitial pressure, leading to muscle breakdown deep around the bony prominences. We proposed the use of intermittent electrical stimulation (IES) as a novel prophylactic method for the prevention of DTI. In this study, we assessed the long-term effectiveness of this technique in pigs that had received a partial spinal cord injury that paralyzed one hindlimb. The pigs recovered for 2 wk postsurgery, and subsequently, their paralyzed limbs were loaded to 25% of their body weights 4 h/day for 4 consecutive days each week for 1 mo. One group of pigs (n = 3) received IES during the loading, whereas another group (n = 3) did not. DTI was quantified using magnetic resonance imaging (MRI) and postmortem histology. In the group that did not receive IES, MRI assessments revealed signs of tissue damage in 48% of the volume of the loaded muscle. In the group that did receive IES, only 8% of the loaded muscle volume showed signs of tissue damage. Similar findings were found through postmortem histology. This study demonstrates, for the first time, that IES may be an effective technique for preventing the formation of DTI in loaded muscles after spinal cord injury.
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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 | 0.000 |
| 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".