Predictors of 1-year mortality in adult lung transplant recipients: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Upon surviving the first year post-lung transplantation, recipients can expect a median survival of 8 years. Within the first year, graft failure and multi-organ failure (possibly secondary to graft failure) are common causes of mortality. To better understand the prognosis within the first year, we plan on conducting a systematic review and meta-analysis of observational studies addressing the association between the patient, donor, and transplant operative factors and graft loss 1-year post-lung transplant. METHODS: We searched MEDLINE, Embase, Cochrane Database of Systematic Reviews, Cochrane Central Register, and PubMed supplemental for non-MEDLINE records for observational studies identifying independent risk factors for early mortality (1 year) in adult lung transplant recipients. We plan on including cohort studies and secondary analyses of randomized controlled trials studying adult lung transplant recipients undergoing their first lung transplant, without any simultaneous organ transplant. We will conduct a random-effects meta-analysis that pools the effect estimates from all eligible studies to obtain a summary estimate and confidence interval for all independent non-therapeutic factors identified in the primary studies. DISCUSSION: The results from this study may inform future guidelines on the selection of candidates and donors for transplantation and predictive model development and inform the decision-making process that the physician and patient undertake together. Furthermore, through the conduction of this review, we can identify the limitations with the current best evidence, which will encourage the need for studies with a better methodology to reassess the predictors of mortality.
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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 | 0.014 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.042 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".