Transbronchial Lung Cryobiopsy and Surgical Lung Biopsy: A Prospective Multi-Centre Agreement Clinical Trial (CAN-ICE)
Bibliographic record
Abstract
Abstract Rationale Transbronchial cryobiopsy (TBCB) for the diagnosis of interstitial lung disease (ILD) has shown promising results, but prospective studies with matched surgical lung biopsy (SLB) have yielded conflicting results. Objectives We aimed to assess within- and between-center diagnostic agreement between TBCB and SLB at both the histopathologic and multidisciplinary discussion (MDD) levels in patients with diffuse ILD. Methods In a multicenter prospective study, we performed matched TBCB and SLB in patients referred for SLB. After a blinded review by three pulmonary pathologists, all cases were reviewed by three independent ILD teams in an MDD. MDD was performed first with TBCB, then with SLB in a second session. Within-center and between-center diagnostic agreement was evaluated using percentages and correlation coefficients. Measurements and Main Results Twenty patients were recruited and underwent contemporaneous TBCB and SLB. Within-center diagnostic agreement between TBCB–MDD and SLB–MDD was reached in 37 of the 60 (61.7%) paired observations, resulting in a Cohen’s κ value of 0.46 (95% confidence interval [CI], 0.29–0.63). Diagnostic agreement increased among high-confidence or definitive diagnoses on TBCB–MDD (21 of 29 [72.4%]), but not significantly, and was more likely among cases with SLB–MDD diagnoses of idiopathic pulmonary fibrosis than fibrotic hypersensitivity pneumonitis (13 of 16 [81.2%] vs. 16 of 31 [51.6%]; P = 0.047). Between-center agreement for cases was markedly higher for SLB–MDD (κ = 0.71 [95% CI, 0.52–0.89]) than TBCB–MDD (κ = 0.29 [95% CI, 0.09–0.49]). Conclusions This study demonstrated moderate TBCB–MDD and SLB–MDD diagnostic agreement for ILD, while between-center agreement was fair for TBCB–MDD and substantial for SLB–MDD. Clinical trial registered with www.clinicaltrials.gov (NCT 02235779).
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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 | 0.018 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".