Evaluation of transcutaneous near-infrared spectroscopy for early detection of cardiac arrest in an animal model
Bibliographic record
Abstract
Abstract Sudden cardiac arrest (SCA) is a leading cause of mortality worldwide. Outcomes highly depend on the SCA-to-resuscitation interval, highlighting the clinical need for quick and reliable SCA detection. Near-infrared spectroscopy (NIRS), a noninvasive optical technique, may have utility for SCA detection. We investigated the ability of hindlimb transcutaneous NIRS to detect changes with pentobarbital-induced cardiac arrest in eight Yucatan miniature pigs. NIRS measurements during cardiac arrest were compared to invasively acquired carotid blood pressure and spinal cord tissue partial oxygen pressure (PO 2 ). We observed statistically significant decreases in mean arterial pressure (MAP) 64.68 mmHg ± 13.08, p = 0.016), spinal cord PO 2 (38.16 mmHg ± 20.04, p = 0.016), and NIRS-derived tissue oxygen saturation (TSI%) (14.50% ± 3.80, p = 0.0078) from baseline to 5 minutes after pentobarbital administration. Euthanasia-to-first change in hemodynamics for MAP and TSI (%) were similar [MAP (10.43 ± 4.73 sec) vs TSI (%) (12.04 ± 1.85 sec), p = 0.605]. No significant difference was detected between NIRS and blood pressure-derived pulse rates during baseline periods ( p > 0.99) and following pentobarbital administration ( p = 0.97). Similar to invasive indices, transcutaneous NIRS demonstrated marked changes with cardiac arrest and was able to rapidly identify hemodynamic changes. Transcutaneous NIRS monitoring may present a novel and noninvasive approach to SCA detection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".