Validating Early Post–Transplant Outcomes Reported for Recipients of Deceased Donor Kidney Transplants
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Data reported to the Organ Procurement and Transplantation Network (OPTN) are used in kidney transplant research, policy development, and assessment of center quality, but the accuracy of early post-transplant outcome measures is unknown. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: The Deceased Donor Study (DDS) is a prospective cohort study at five transplant centers. Research coordinators manually abstracted data from electronic records for 557 adults who underwent deceased donor kidney transplantation between April of 2010 and November of 2013. We compared the post-transplant outcomes of delayed graft function (DGF; defined as dialysis in the first post-transplant week), acute rejection, and post-transplant serum creatinine reported to the OPTN with data collected for the DDS. RESULTS: Median kidney donor risk index was 1.22 (interquartile range [IQR], 0.97-1.53). Median recipient age was 55 (IQR, 46-63) years old, 63% were men, and 47% were black; 93% had received dialysis before transplant. Using DDS data as the gold standard, we found that pretransplant dialysis was not reported to the OPTN in only 11 (2%) instances. DGF in OPTN data had a sensitivity of 89% (95% confidence interval [95% CI], 84% to 93%) and specificity of 98% (95% CI, 96% to 99%). Surprisingly, the OPTN data accurately identified acute allograft rejection in only 20 of 47 instances (n=488; sensitivity of 43%; 95% CI, 17% to 73%). Across participating centers, sensitivity of acute rejection varied widely from 23% to 100%, whereas specificity was uniformly high (92%-100%). Six-month serum creatinine values in DDS and OPTN data had high concordance (n=490; Lin concordance correlation =0.90; 95% CI, 0.88 to 0.92). CONCLUSIONS: OPTN outcomes for recipients of deceased donor kidney transplants have high validity for DGF and 6-month allograft function but lack sensitivity in detecting rejection. Future studies using OPTN data may consider focusing on allograft function at 6 months as a useful outcome.
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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 | 0.025 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".