Oesophageal stasis is a risk factor for chronic lung allograft dysfunction and allograft failure in lung transplant recipients
Bibliographic record
Abstract
Background: Morbidity and mortality in lung transplant recipients are often triggered by recurrent aspiration events, potentiated by oesophageal and gastric disorders. Previous small studies have shown conflicting associations between oesophageal function and the development of chronic lung allograft dysfunction (CLAD). Herein, we sought to investigate the relationship between oesophageal motility disorders and long-term outcomes in a large retrospective cohort of lung transplant recipients. Methods: All lung transplant recipients at the Toronto Lung Transplant Program from 2012 to 2018 with available oesophageal manometry testing within the first 7 months post-transplant were included in this study. Patients were categorised according to the Chicago Classification of oesophageal disorders (v3.0). Associations between oesophageal motility disorders with the development of CLAD and allograft failure (defined as death or re-transplantation) were assessed. Results: Of 487 patients, 57 (12%) had oesophagogastric junction outflow obstruction (OGJOO) and 47 (10%) had a disorder of peristalsis (eight major, 39 minor). In a multivariable analysis, OGJOO was associated with an increased risk of CLAD (HR 1.71, 95% CI 1.15-2.55, p=0.008) and allograft failure (HR 1.69, 95% CI 1.13-2.53, p=0.01). Major disorders of peristalsis were associated with an increased risk of CLAD (HR 1.55, 95% CI 1.01-2.37, p=0.04) and allograft failure (HR 3.33, 95% CI 1.53-7.25, p=0.002). Minor disorders of peristalsis were not significantly associated with CLAD or allograft failure. Conclusion: Lung transplant recipients with oesophageal stasis characterised by OGJOO or major disorders of peristalsis were at an increased risk of adverse long-term outcomes. These findings will help with risk stratification of lung transplant recipients and personalisation of treatment for aspiration prevention.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".