Measuring severe lung disease in mice - a cautionary tale
Bibliographic record
Abstract
Background: The flexiVent small animal ventilator (SCIREQ, Canada) allows measurement of detailed respiratory mechanics on animals as small as 10g. It uses the forced oscillation technique (FOT) to measure respiratory system impedence (Zrs) by applying a signal containing 19 mutually prime sinusoidal frequencies ranging from 0.25 to 19.625 Hz at the airway opening. The constant phase model (CPM) is fit to Zrs to calculate airway resistance (Raw) tissue damping (G) and tissue elastance (H). The default settings of flexiVent apply an unweighted (absolute) fitting of the CPM to the Zrs spectra. This may not be appropriate for mice with severe lung disease. Methods: Adult female BALB/c mice with influenza-induced lung disease were challenged with methacholine (MCh). Data were analysed using the default primewave settings of flexiVent v5.1. These same data were then adjusted to remove the two lowest frequencies (0.25 and 0.625 Hz) and a relative (weighted) fitting of the CPM was applied. Results: The default analysis produced a poor model fit and uninterpretable measurements of Raw, G and H, especially at higher MCh concentrations.Applying the weighted fitting improved the application of the CPM to Zrs spectra. A better partitioning of the CPM into airway and tissue compartments was achieved by removing the lowest two frequencies from the analysis. Conclusion: The default flexiVent settings provided by SCIREQ are not suitable for accurate measurement of lung mechanics in mouse models of severelung disease.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.012 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".