Oscillometry of the respiratory system: a translational opportunity not to be missed
Bibliographic record
Abstract
Airway oscillometry has become the de facto standard for quality assessment of lung physiology in laboratory animals and has demonstrated its usefulness in understanding diseases of small airways. Nowadays, it is seeing extensive use in daily clinical practice and research; however, a question that remains unanswered is how well physiological findings in animals and humans correlate? Methodological and device differences are obvious between animal and human studies. However, all devices deliver an oscillated airflow test signal and output respiratory impedance. In addition, despite analysis differences, there are ways to interpret animal and human oscillometry data to allow suitable comparisons. The potential with oscillometry is its ability to reveal universal features of the respiratory system across species, making translational extrapolation likely to be predictive. This means that oscillometry can thus help determine if an animal model displays the same physiological characteristics as the human disease. Perhaps more importantly, it can also be useful to determine whether an intervention is effective as well as to understand if it affects the desired region of the respiratory system, e.g., the periphery of the lung. Finally, findings in humans can also inform preclinical scientists and give indications as to what type of physiological changes should be observed in animal models to make them relevant as models of human disease. The present article will attempt to demonstrate the potential of oscillometry in respiratory research, an area where the development of novel therapies is plagued with a failure rate higher than in other disease areas.
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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 | 0.017 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.007 | 0.020 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".