Real-time and non-invasive acute lung rejection diagnosis using confocal LASER Endomicroscopy in lung transplant recipients: Results from the CELTICS study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Traditionally, the diagnosis of acute rejection (AR) relies on invasive transbronchial biopsies (TBBs) to obtain histopathological samples. We aimed to evaluate the diagnostic yield of probe-based confocal laser endomicroscopy (pCLE) as a complementary and non-invasive tool for ACR screening, comparing its results with those obtained from TBBs. METHODS: Between January 2015 and April 2022, we conducted a retrospective study of all lung transplant recipients aged over 18 years at Toulouse University Hospital (France). All patients who underwent bronchoscopies with both TBBs and pCLE imaging were included. Two experienced interpreters (TV and MS) reviewed the pCLE images independently, blinded to all clinical information and pathology results. RESULTS: <0.0001 and 0.003 respectively). Alveolar cellularity demonstrated a sensitivity (Se) of 85.3 %, specificity (Spe) of 43 %, positive predictive value (PPV) of 37.2 % and negative predictive value (NPV) of 88.1 %. For PVC, Se was 70.6 %, Spe 80.2 %, PPV 58.5 % and NPV 87.3 %. Intra-interpreter correlation (TV) was 88.3 % for the number of vessels (+/-1), 98.3 % for ALC and 90 % for PVC. Inter-interpreter correlation (TV and MS) was 80 % for vessels (+/-1), 97.5 % for ALC and 83.3 % for PVC. CONCLUSION: Our study demonstrates the feasibility of incorporating pCLE into clinical practice, demonstrating good diagnostic yield and reproducible outcomes in the screening of AR in lung transplant recipients.
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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 | 0.003 | 0.004 |
| 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 | 0.001 | 0.000 |
| 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".