Bronchiolitis Obliterans Syndrome in Lung Transplant Recipients: Can Thin-Section CT Findings Predict Disease before Its Clinical Appearance?
Bibliographic record
Abstract
PURPOSE: To determine whether there are thin-section computed tomographic (CT) features that predict bronchiolitis obliterans syndrome (BOS) in lung transplant recipients before the clinical appearance and during the early stages of the disease. MATERIALS AND METHODS: Two hundred ninety-eight thin-section CT scans obtained in 26 lung transplant recipients who did (study group) and 26 lung transplant recipients who did not (control group) develop BOS were reviewed for the presence of mosaic perfusion, bronchiectasis, bronchial wall thickening, and air trapping. BOS was defined by using the recently revised definition of the International Society for Heart and Lung Transplantation. CT scans obtained in the BOS group were divided into three groups: Group A consisted of the last scans obtained before the clinical appearance of BOS; groups B and C consisted of, respectively, the first and last scans obtained after the clinical appearance of BOS. Scans obtained in the control group were acquired during similar posttransplantation periods and matched to scans in each BOS group. Sensitivity, specificity, and positive and negative predictive values were calculated separately for each subgroup. The optimal threshold for each thin-section CT-depicted abnormality was defined by using receiver operating characteristics analysis. RESULTS: The sensitivities of air trapping for the diagnosis of BOS during the periods in which the scans in groups A, B, and C were obtained were 50%, 44%, and 64%, respectively; specificities were 80%, 100%, and 80% respectively. Sensitivities of mosaic perfusion were 4%, 20%, and 36%, respectively; specificities were 100%, 96%, and 96%, respectively. Sensitivities of bronchiectasis were 25%, 24%, and 32%, respectively; specificities were 80%, 80%, and 96%, respectively. Sensitivities of bronchial wall thickening were 4%, 24%, and 40%, respectively; specificities were 96%, 84%, and 80%, respectively. Air trapping was seen intermittently in nine (43%) of 21 patients with CT scans that depicted this finding at least once. CONCLUSION: The value of the finding of air trapping before the clinical appearance and during the early stages of BOS is lower than has been previously reported. When using the recently revised criteria for BOS, the role of thin-section CT as a screening test to evaluate patients with lung transplants appears to be limited.
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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 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".