Bronchiolitis obliterans following lung transplantation: early detection using computed tomographic scanning
Bibliographic record
Abstract
BACKGROUND: Computed tomographic (CT) scanning may enable earlier diagnosis of chronic lung allograft dysfunction than forced expiratory volume in 1 second (FEV1). A study was undertaken to determine intra-observer and inter-observer agreement of composite and air trapping CT scores, to examine the association of FEV1 with the composite and air trapping CT score, and to relate the baseline composite CT score to changes in FEV1 and changes in the composite CT score over 1 year. METHODS: Lung function and baseline CT scans following transplantation and at subsequent annual follow ups were analysed in 38 lung transplant recipients. Scans were randomly scored by two observers for bronchiectasis, mucus plugging, airway wall thickening, consolidation, mosaic pattern, and air trapping, and re-scored after 1 month. CT scores were expressed on a scale of 0-100 and correlated with FEV1 as a percentage of the post-transplant baseline value. RESULTS: The mean (SD) interval between baseline and follow up CT scans was 11.2 (4.7) months. Inter-observer and intra-observer agreement was good for both the composite and air trapping CT scores. There was a significant association between FEV1 and the composite CT score, with each unit of worsening in the baseline composite CT score predicting a 1.55% and 1.37% worsening in FEV1 over the following year (p<0.0001) and a 1.25 and 1.12 unit worsening in the composite CT score (p<0.0001) for observers 1 and 2, respectively. CONCLUSION: These findings indicate a potential role for a composite CT scoring system in the early detection of bronchiolitis obliterans.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".