Diaphragm blood flow: new avenues for human translation
Bibliographic record
Abstract
The rhythmic contraction of the diaphragm facilitates continuous pulmonary ventilation essential for life. Adequate blood flow to the diaphragm is critical to continuously support contractile function, as an imbalance in nutritive supply and demand can lead to diaphragm insufficiency, patient morbidity, and mortality. Given oxygen supply to the diaphragm is key to its function, it is no surprise that more than 200 animal studies have investigated diaphragm blood flow ([Formula: see text]) regulation over the past century. This work has advanced our understanding of the diaphragm's circulatory control (i.e., regional blood flow heterogeneity and mechanical impediment) and response to a variety of conditions, including eupnea, exercise, hypoxia, hypercapnia, hemorrhage, mechanical ventilation, and pharmacological interventions. However, due to the relative inaccessibility of the diaphragm, few studies have been conducted in humans since [Formula: see text] measurements have historically required highly invasive and technically challenging techniques that are not conducive to routine use. Thus, our current understanding of [Formula: see text] is informed almost exclusively by animal work with conflicting findings, and its translation to humans is hindered by species-dependent variability in diaphragmatic structure and function. Novel approaches have been developed to quantify respiratory muscle blood flow in humans using minimally invasive techniques. More recently, contrast-enhanced ultrasound (CEUS) is a promising approach for quantifying [Formula: see text] in humans, independent from other respiratory muscles. Using novel approaches to quantify [Formula: see text] in humans, future research can aim to advance our understanding of [Formula: see text] in humans in health and disease, including exercise, sex-based comparisons, and critical care.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.051 | 0.014 |
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".