Optical coherence tomography (OCT) of chronic lung allograft dysfunction (CLAD)
Bibliographic record
Abstract
This study explores endobronchial optical coherence tomography (OCT) imaging of lung transplant patients with chronic lung allograft dysfunction (CLAD). Optical coherence tomography (OCT), the optical analog of ultrasonography with superior resolution (10μm) but shallow (2mm) penetration, allows for the visualization of the early structural changes in the small airways, which is of interest in CLAD progression.Imaging was conducted with a catheter-based rotary OCT probe during routine bronchoscopy procedures, resulting in three-dimension pullbacks of three subsegmental airways per patient (n=9). A scoring rubric for visualized features of interest was used to quantify characteristics of the image set: loss of alveolar visualization, emphysema-like alveolar enlargement, alveolar hyperinflation, airway dilation, excessive mucous, excessive duct-like structures, and an unidentified structure. Four raters, blinded to clinical status, scored the set. Statistical analysis including Pearson correlation coefficients (R), Fleiss’ Kappa (κ) were used on this score set to assess preliminary potential of these features.3/9 patients met the diagnostic criteria for both obstructive (BOS) and restrictive (RAS) phenotypes of CLAD and 6/9 for solely the obstructive phenotype. The airway dilation feature was found to be significantly associated (p<0.05) with the BOS+RAS diagnosis for three raters (R=0.72-0.94), with fairly consistent rater reliability (κinterrater = 0.25, κintrarater = 0.59). No OCT features were significantly correlated with infection status.Small airway dilation, as measured through catheterized OCT imaging, shows potential for use in detection of CLAD and distinguishing between CLAD phenotypes.
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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 | 0.001 |
| 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".