Blood flow index using near-infrared spectroscopy and indocyanine green as a minimally invasive tool to assess respiratory muscle blood flow in humans
Bibliographic record
Abstract
Near-infrared spectroscopy (NIRS) in combination with indocyanine green (ICG) dye has recently been used to measure respiratory muscle blood flow (RMBF) in humans. This method is based on the Fick principle and is determined by measuring ICG in the respiratory muscles using transcutaneous NIRS in relation to the [ICG] in arterial blood as measured using photodensitometry. This method is invasive since it requires arterial cannulation, repeated blood withdrawals, and reinfusions. A less invasive alternative is to calculate a relative measure of blood flow known as the blood flow index (BFI), which is based solely on the NIRS ICG curve, thus negating the need for arterial cannulation. Accordingly, the purpose of this study was to determine whether BFI can be used to measure RMBF at rest and during voluntary isocapnic hyperpnea at 25, 40, 55, and 70% of maximal voluntary ventilation in seven healthy humans. BFI was calculated as the change in maximal [ICG] divided by the rise time of the NIRS-derived ICG curve. Intercostal and sternocleidomastoid muscle BFI were correlated with simultaneously measured work of breathing and electromyography (EMG) data from the same muscles. BFI showed strong relationships with the work of breathing and EMG for both respiratory muscles. The coefficients of determination (R(2)) comparing BFI vs. the work of breathing for the intercostal and sternocleidomastoid muscles were 0.887 (P < 0.001) and 0.863 (P < 0.001), respectively, whereas the R(2) for BFI vs. EMG for the intercostal and sternocleidomastoid muscles were 0.879 (P < 0.001) and 0.930 (P < 0.001), respectively. These data suggest that the BFI closely reflects RMBF in conscious humans across a wide range of ventilations and provides a less invasive and less technically demanding alternative to measuring RMBF.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".