A clinical tool to calculate post‐transplant survival using pre‐transplant clinical characteristics in adults with cystic fibrosis
Bibliographic record
Abstract
Abstract Background We previously identified factors associated with a greater risk of death post‐transplant. The purpose of this study was to develop a clinical tool to estimate the risk of death after transplant based on pre‐transplant variables. Methods We utilized the Canadian CF registry to develop a nomogram that incorporates pre‐transplant clinical measures to assess post‐lung transplant survival. The 1‐, 3‐, and 5‐year survival estimates were calculated using Cox proportional hazards models. Results Between 1988 and 2012, 539 adult Canadians with CF received a lung transplant with 208 deaths in the study period. Four pre‐transplant factors most predictive of poor post‐transplant survival were older age at transplantation, infection with B. cepacia complex, low FEV1 percent predicted, and pancreatic sufficiency. A nonlinear relationship was found between risk of death and FEV1 percent predicted, age at transplant, and BMI. We constructed a risk calculator based on our model to estimate the 1‐, 3‐, and 5‐year probability of survival after transplant which is available online. Conclusions Our risk calculator quantifies the risk of death associated with lung transplant using pre‐transplant factors. This tool could aid clinicians and patients in the decision‐making process and provide information regarding the timing of lung transplantation.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".