Predicting donor lung acceptance for transplant during ex vivo lung perfusion: The EX vivo lung PerfusIon pREdiction (EXPIRE)
Bibliographic record
Abstract
Ex vivo lung perfusion (EVLP) has being increasingly used for the pretransplant assessment of extended-criteria donor lungs. Mathematical models to predict lung acceptance during EVLP have not been reported so far. Thus, we hypothesized that predictors of lung acceptance could be identified and used to develop a mathematical model describing the clinical decision-making process used in our institution. Donor lungs characteristics and EVLP physiologic parameters included in our EVLP registry were examined (derivation cohort). Multivariable logistic regression analysis was performed to identify predictors independently associated with lung acceptance. A mathematical model (EX vivo lung PerfusIon pREdiction [EXPIRE] model) for each hour of EVLP was developed and validated using a new cohort (validation cohort). Two hundred eighty donor lungs were assessed with EVLP. Of these, 186 (66%) were accepted for transplantation. ΔPO2 and static compliance/total lung capacity were identified as independent predictors of lung acceptance and their respective cut-off values were determined. The EXPIRE model showed a low discriminative power at the first hour of EVLP assessment (AUC: 0.69 [95% CI: 0.62–0.77]), which progressively improved up to the fourth hour (AUC: 0.87 [95% CI: 0.83–0.92]). In a validation cohort, the EXPIRE model demonstrated good discriminative power, peaking at the fourth hour (AUC: 0.85 [95% CI: 0.76–0.94]). The EXPIRE model may help to standardize lung assessment in centers using the Toronto EVLP technique and improve overall transplant rates.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".