Hemodynamics of the sternocleidomastoid measured with frequency domain near-infrared spectroscopy towards non-invasive monitoring during mechanical ventilation
Bibliographic record
Abstract
Mechanical ventilation (MV) is used to assist spontaneous breathing in critically ill patients in the intensive care unit (ICU). MV is a cornerstone of critical care medicine but it is now known that inspiratory muscle dysfunction due to injury, disuse, and/or atrophy during MV plays a major role in outcomes for these patients. For example, prolonged MV is strongly correlated with dysfunction of the sternocleidomastoid (SCM), an accessory inspiratory muscle that has been linked to weaning failure from MV. Hemodynamic monitoring of the SCM may provide an important non-invasive and real-time means to monitor MV. In this work, we first conducted multi-layer Monte Carlo simulations to confirm the ability of near infrared light to detect changes in the oxygenation of the SCM over wide ranges of skin tones and adipose layer thicknesses. We then optimized a custom digital frequency domain near-infrared spectroscopy (FD-NIRS) system for continuous 10 Hz measurements of the SCM at 730 nm and 850 nm. A healthy volunteer study was conducted (N=10); subjects performed sets of isometric neck flexions of the SCM. Substantial changes in oxyhemoglobin + oxymyoglobin (oxy[Hb + Mb]), deoxyhemoglobin + deoxymyoglobin (deoxy[Hb + Mb]), and total hemoglobin + myoglobin (total[Hb + Mb]) were observed during sustained and intermittent isometric flexions. There were notable sex differences observed in the magnitude of hemodynamic changes (∼2x larger changes in males for oxy[Hb + Mb] and deoxy[Hb + Mb]). The magnitude of hemodynamic changes when taking into account µ s ′ changes during flexions was ∼ 2-2.5x larger as compared to assuming constant scattering (CS), which is a common assumption used for continuous wave (CW) NIRS methods. This study suggests that FD-NIRS provides improved accuracy for hemodynamic monitoring of the SCM compared to CW-NIRS, and that FD-NIRS may provide value for SCM monitoring during MV.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".