Outcomes after flow cytometry crossmatch-positive lung transplants managed with perioperative desensitization
Bibliographic record
Abstract
Our program previously reported successful outcomes following virtual crossmatch (VXM)-positive lung transplants managed with perioperative desensitization, but our ability to stratify their immunologic risk was limited without flow cytometry crossmatch (FCXM) data before 2014. The aim of this study was to determine allograft and chronic lung allograft dysfunction (CLAD)-free survival following VXM-positive/FCXM-positive lung transplants, which are performed at a minority of programs due to the high immunologic risk and lack of data on outcomes. All first-time lung transplant recipients between January 2014 and December 2019 were divided into 3 cohorts: VXM-negative (n = 764), VXM-positive/FCXM-negative (n = 64), and VXM-positive/FCXM-positive (n = 74). Allograft and CLAD-free survival were compared using Kaplan-Meier and multivariable Cox proportional hazards models. Five-year allograft survival was 53% in the VXM-negative cohort, 64% in the VXM-positive/FCXM-negative cohort, and 57% in the VXM-positive/FCXM-positive cohort (P = .7171). Five-year CLAD-free survival was 53% in the VXM-negative cohort, 60% in the VXM-positive/FCXM-negative cohort, and 63% in the VXM-positive/FCXM-positive cohort (P = .8509). This study confirms that allograft and CLAD-free survival of patients who undergo VXM-positive/FCXM-positive lung transplants with the use of our protocol does not differ from those of other lung transplant recipients. Our protocol for VXM-positive lung transplants improves access to transplant for sensitized candidates and mitigates even high immunologic risk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".