Development and validation of human airway analysis algorithm using multidetector row CT
Bibliographic record
Abstract
There is currently no accurate method to measure airway dimensions on multidetector row computed tomography (multi-slice CT). We developed CT image analysis software to measure airway lumenal area (Ai) and airway wall area (Aaw) and compared these with quantitative morphology of excised human lungs as the gold standard. Airways identified on the CT images (1.25 mm collimation) were matched to airways identified on the lung's cut surface and Ai and Aaw were measured using custom software. The measured morphological airway lumen ranged from 1.0 to 6.4 mm in diameter. Airway dimensions obtained from CT data correlated with morphologic measurements (r = 0.96 for Ai and r = 0.91 for Aaw). However the CT systematically underestimated Ai and overestimated Aaw; average error (100 x (CT-morphology) / morphology) was -55% for Ai and +90% for Aaw. We used the morphology data to correct the CT measurements and reduced the average error to +23% for Ai and +7% for Aaw. This algorithm can be used to assess the structure and function of human airways.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".