Non-invasive whole-body plethysmograph for assessment and prediction of radiation-induced lung injury using simultaneously acquired nitric oxide and lung volume measurements
Bibliographic record
Abstract
Radiation-induced lung injury (RILI) is a prevalent side effect in patients who undergo thoracic irradiation as part of their cancer treatment. Preclinical studies play a major role in understanding disease onset under controlled experimental conditions. The aim of this work is to develop a single-chambered optimized, non-invasive, whole-body plethysmograph prototype for unrestrained small animal lung volume measurements for preclinical RILI studies. The system is also designed to simultaneously obtain nitric oxide (NO) measurements of the expired breath. The device prototype was tested using computer simulations, phantom studies and in vivo measurements in experimental animal models of RILI. The system was found to improve resemblance to true breathing signal characteristics as measured by improved skewness (21.83%) and kurtosis (51.94%) in addition to increased overall signal sensitivity (3.61%) of the acquired breath signal, when compared to matching control data. NO concentration data was combined with breath measurements in order to predict early RILI onset. The system was evaluated using serial weekly measurements in hemi-thorax irradiated rats (n = 8) yielding a classification performance of 50.0%, 62.5%, 87.5% using lung volume only, NO only, and combined measurements of both, respectively. Our results indicate that improved performance could be achieved when measurements of lung volume are combined with those of NO. This would provide the overall plethysmography system with the ability to provide useful diagnostic and prognostic information for preclinical and, potentially, clinical thoracic dose escalation studies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".