Quantitative chest <scp>CT</scp> for subtyping chronic lung allograft dysfunction and its association with survival
Bibliographic record
Abstract
Chronic lung allograft dysfunction (CLAD) is a major cause of mortality in lung transplant recipients. CLAD can be sub-divided into at least 2 subtypes with distinct mortality risk characteristics: restrictive allograft syndrome (RAS), which demonstrates increased overall computed tomography (CT) lung density in contrast with bronchiolitis obliterans syndrome (BOS), which demonstrates reduced overall CT lung density. This study aimed to evaluate a reader-independent quantitative density metric (QDM) derived from CT histograms to associate with CLAD survival. A retrospective study evaluated CT scans corresponding to CLAD onset using pulmonary function tests in 74 patients (23 RAS, 51 BOS). Two different QDM values (QDM1 and QDM2) were calculated using CT lung density histograms. Calculation of QDM1 includes the extreme edges of the histogram. Calculation of QDM2 includes the central region of the histogram. Kaplan-Meier analysis and Cox regression analysis were used for CLAD prognosis. Higher QDM values were significantly associated with decreased survival. The hazard ratio for death was 3.2 times higher at the 75th percentile compared to the 25th percentile using QDM1 in a univariate model. QDM may associate with CLAD patient prognosis.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".