Repeatability of real world, non-research chest CT scan-based lung density metrics
Bibliographic record
Abstract
Quantitative CT (QCT) is carried out with attention to quality control (scanner make and model, calibration, lung volume and acquisition protocol), and bears financial and radiation cost. We wished to determine if non-research CT scans without such controls could yield reproducible data. 62 subjects (53 COPD, 6 asthma, 2 micronodules, 1 sarcoidosis) from a community respirology practice had had 2 non-contrast CT scans judged free of significant infiltrates, performed on 5 models of scanner in 9 different community hospitals for clinical indications within 14 months and had available spirometry and lung volumes performed respecting ATS criteria within 13 months of CT scans. Images were analyzed with AirwayInspector (airwayinspector.acil-bwh.org) for LAA<-950HU, lung density (LD) at 15th percentile + 1000HU and total lung volume (TLV). Means, Bland-Altman analysis and intraclass correlation coefficients were determined for TLV, LAA, LD and LD corrected for both predicted and measured TLC. Differences were determined with Student t-tests. Significance was set at p<05. Results are shown in Table 1. Real-world CT scans, if properly selected, can yield reproducible QCT data. Correcting LD with PFT measured TLC improves reproducibility more than correcting with predicted TLC. If validated at other centres, these findings suggest the pool of observational QCT data could be vastly expanded at little dollar and no radiation cost.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".