Near-Infrared Spectroscopy in Lower Extremity Trauma
Bibliographic record
Abstract
BACKGROUND: Near-infrared spectroscopy measures the percentage of hemoglobin oxygen saturation in the microcirculation of tissue up to 3 cm below the skin. The purpose of this study was to describe the measurable response of normal tissue oxygenation in the leg after acute trauma with use of this technique. METHODS: Twenty-six patients with acute unilateral tibial fractures and twenty-five uninjured volunteer control subjects were enrolled. Near-infrared spectroscopy measurements were obtained for both legs in all four compartments: anterior, lateral, deep posterior, and superficial posterior. The twenty-six injured legs were compared with twenty-five uninjured legs (randomly selected) of the volunteer control group, with the contralateral limb in each patient serving as an internal control. RESULTS: The mean tissue oxygenation for each compartment in the injured legs was 69% (anterior), 70% (lateral), 74% (deep posterior), and 70% (superficial posterior). In the control (uninjured) legs, the average tissue oxygenation percentage in each compartment was 54%, 55%, 60%, and 57%, respectively. Repeated-measures analysis revealed that near-infrared spectroscopy values averaged 15.4 percentage points (95% confidence interval, 12.2 to 18.6 percentage points) higher for injured legs than for uninjured legs, controlling for the value of the contralateral limb (p < 0.0001). CONCLUSIONS: Tibial fracture produces a predictable increase in tissue oxygenation as measured by near-infrared spectroscopy. The corresponding compartment of the contralateral leg can provide strong utility as an internal control value when evaluating the hyperemic response to 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".