Case Report: Delayed Lung Transplantation With Intraoperative ECMO Support for Herbicide Intoxication-Related Irreversible Pulmonary Fibrosis: Strategy and Outcome
Bibliographic record
Abstract
Background: Lung transplantation is recognized as the only therapeutic option for patients who develop irreversible pulmonary fibrosis after herbicide intoxication. Methods: We have collected and presented clinical course and outcome of four patients who received lung transplantation due to paraquat and diquat intoxication from 2018 to 2021. Another patient who received initial lung transplantation due to paraquat intoxication and re-transplantation due to chronic lung allograft dysfunction in 2019, was further reported. Patients were admitted in lung transplantation centers, including the 1st affiliated hospital of Zhengzhou University and Wuxi Lung transplantation center. Previous reported cases from Europe, Canada and China were also summarized as benchmark. Results: During the period from the year of 2018 to 2021, there have been four patients in China, who received lung transplantation due to herbicide intoxication. Median age of the four patients was 37 (IQR 34.5, 39.75) years old. Median time from intoxication to lung transplantation was 27.5 (IQR 27, 30.5) days. Bilateral lung transplantation was performed in three patients, while one single lung transplantation was performed in an urgent listed patient. Extracorporeal Membrane Oxygenation (ECMO) and hemopurification support were used in all patients (100%). Details of the cases with follow-ups were further presented and analyzed. Conclusions: Late timing of bilateral lung transplantation can be performed successfully for pulmonary fibrosis after paraquat or diquat intoxication. The survival of patients with complex perioperative conditions can be achieved with a multidisciplinary team to manage the irreversible effects of intoxication.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".