Bibliographic record
Abstract
FIBROSIS (IPF): ENHANCES PROGNOSTICATION ACCURACYIPF is a clinically and radiologically heterogenous condition.AIQCT is a novel tool developed, in part, to account for the interobserver variability seen when reporting parenchymal abnormalities on CT in IPF.Handa et al (Ann Am Thorac Soc 2022;19:399) used AIQCT in a training cohort of 304 CTs from patients with IPF to derive prognostic features, and then applied to 120 consecutively enrolled patients in a validation cohort.Patients required paired pulmonary function tests within 3 months of scanning, and scans could not demonstrate pleural effusion, pneumomediastinum or acute exacerbation of IPF (AE-IPF).Correlation coefficients outperformed those of texture-based analysis methods (p≤0.001 for 7/9 radiographic patterns), such as Canadian Laboratory Initiative on Paediatric Intervals, and reflects the reliability of AIQCT for quantification of lung abnormalities seen in other studies of the modality.Strengths of this protocol include the ability to differentiate traction bronchodilatation from honeycombing, and the automatic volumetric measurement of airways to include peripheral airways.AIQCT not only reliably identifies lung parenchymal patterns but also bronchial and central airways volumes.Multivariate Cox regression analysis including gender-age physiology staging found that bronchial volume (ie, the presence of traction bronchodilatation) and normal (noninterstitial lung disease (ILD)) lung volumes were independent prognostic factors in IPF (HRs 1.33, 95% CI 1.16 to 1.53, and 0.97, 95% CI 0.94 to 0.99, respectively).This study demonstrates the potential for novel software to enhance clinical care by providing better information to clinicians on longer-term outcomes but it requires further research prior to adoption into clinical practice.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.705 | 0.607 |
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".