Cumulative Deficits Frailty Index Predicts Outcomes for Solid Organ Transplant Candidates
Bibliographic record
Abstract
Background. Despite comprehensive multidisciplinary candidacy assessments to determine appropriateness for solid organ transplantation, limitations persist in identifying candidates at risk of adverse outcomes. Frailty measures may help inform candidacy evaluation. Our main objective was to create a solid organ transplant frailty index (FI), using the cumulative deficits model, from data routinely collected during candidacy assessments. Secondary objectives included creating a social vulnerability index (SVI) from assessment data and evaluating associations between the FI and assessment, waitlist, and posttransplant outcomes. Methods. In this retrospective cohort study of solid organ transplant candidates from Toronto General Hospital, cumulative deficits FI and SVI were created from data collected during candidacy evaluations for consecutive kidney, heart, liver, and lung transplant candidates. Regression modeling measured associations between the FI and transplant listing, death or removal from the transplant waitlist, and survival after waitlist placement. Results. For 794 patients, 40 variable FI and 10 variable SVI were created (258 lung, 222 kidney, 201 liver, and 113 heart transplant candidates). The FI correlated with assessment outcomes; patients with medical contraindications (mean FI 0.35 ± 0.10) had higher FI scores than those listed (0.29 ± 0.09), P < 0.001. For listed patients, adjusted for age, sex, transplant type, and SVI, higher FI was associated with an increased risk of death (pretransplant or posttransplant) or delisting (hazard ratio 1.03 per 0.01 FI score, 95% confidence interval, 1.01-1.05, P = 0.01). Conclusions. A cumulative deficits FI can be derived from routine organ transplant candidacy evaluations and may identify candidates at higher risk of adverse outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".