Establishing a Core Outcome Measure for Graft Health
Bibliographic record
Abstract
SONG-Tx. Background Graft loss, a critically important outcome for transplant recipients, is variably defined and measured and incompletely reported in trials. We convened a consensus workshop on establishing a core outcome measure for graft loss for all trials in kidney transplantation. Methods Twenty-five kidney transplant recipients/caregivers and 33 health professionals from eight countries participated. Transcripts were analyzed thematically. Results Five themes were identified. “Graft loss as a continuum” conceptualizesgraft loss as a process, but requiring an endpoint defined as a discrete event. In “defining an event with precision and accuracy,” loss of graft function requiring chronic dialysis(minimum 90 days)provided an objective and practical definition; re-transplant would capture pre-emptive transplantation; relisting was readily measured but would overestimate graft loss; and allograft nephrectomy was redundant in being preceded by dialysis. However, the thresholds for renal replacement therapy varied. Conservative management was regarded as too ambiguous and complexto use routinely. “Distinguishing death-censored graft loss” would ensure clarity and meaningfulness in interpreting results. “Consistent reporting for decision-making” by specifying time points and metrics (i.e. time to event) was suggested. “Ease of ascertainment and data collection” of the outcome from registries could support use of registry data to efficiently extend follow-up of trial participants. Conclusions A practical and meaningful core outcome measure for graft loss may be defined as chronic dialysis or re-transplant, and distinguished from loss due to death. Consistent reporting of graft loss using standardized metrics and time points may improve the contribution of trials to decision-making in kidney 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 | 0.156 | 0.173 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".