Hemodynamic and oxidative mechanisms of tourniquet-induced muscle injury: near-infrared spectroscopy for the orthopedics setting
Bibliographic record
Abstract
During orthopedic procedures, the tourniquets used to maintain bloodless surgical fields cause ischemia and then reperfusion (I/R), leading to oxidative muscle injury. Established methods exist neither for monitoring orthopedic I/R nor for predicting the extent of tourniquet-associated oxidative injury. To develop a predictive model for tourniquet-associated oxidative muscle injury, this study combined real-time near-infrared spectroscopy (NIRS) monitoring of I/R with Western blotting (WB) for oxidized proteins. We hypothesized strong correlations between NIRS-derived I/R indices and muscle protein oxidation. In 17 patients undergoing ankle fracture repair, a thigh tourniquet was inflated on the injured limb (300 mmHg). Using a continuous-wave (CW) NIRS setup, oxygenated (O2Hb), deoxygenated (HHb), and total (tHb) hemoglobin were monitored bilaterally (tourniquet versus control) in leg muscles. Leg muscle biopsies were collected unilaterally (tourniquet side) immediately after tourniquet inflation (pre) and before deflation (post). Average ischemia duration was 43.2 ± 14.6 min. In post-compared to pre-biopsies, muscle protein oxidation (quantified using WB) increased 172.3%± 145.7% (P<0.0005). Changes in O2Hb and tHb were negatively correlated with protein oxidation (respectively: P=0.040, R2=0.25 and P=0.003, R2=0.58). Reoxygenation rate was positively correlated with protein oxidation (P=0.041, R2=0.25). These data indicate that using CW NIRS, it is possible to predict orthopedic tourniquet-associated muscle oxidative injury noninvasively.
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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".