Effect of the type of end-stage lung disease on postoperative evolution of lung transplantation patients
Bibliographic record
Abstract
Introduction : The postoperative period is a critical time in lung transplantation. It has already been established that the type of end-stage lung disease has an impact on the survival benefit after lung transplantation. However, the specific aspects of postoperative evolution such as mechanical ventilation duration (MVD), intensive care unit length of stay (ICU LOS), and hospital LOS have not been extensively studied. Methods: We reviewed the experience in our center through a retrospective chart analysis of 201 patients who underwent lung transplantation from 2004 to 2010. We investigated if the MVD, ICU LOS, hospital LOS and the need for a tracheotomy are related to the underlying end-stage lung disease. We analyzed the data with descriptive statistics to identify differences between subgroups. Access to medical charts was granted by the Research Ethics Committee. Results: Our results show that Idiopathic Pulmonary Fibrosis (IPF) patients have much longer MVD, ICU LOS and hospital LOS, require more tracheotomies and have higher mortality in the postoperative period when compared to other end-stage lung diseases. (Table 1) Conclusion: Patients with IPF have longer ICU LOS and this seems to be related to longer MVD. Further research could address methods of improving weaning from mechanical ventilation in these patients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".