Establishing a Core Outcome Measure for Life Participation
Bibliographic record
Abstract
Background Life participation is a critically important outcome for kidney transplant recipients but it is inconsistently and infrequently measured in trials. We convened a consensus workshop on establishing a core outcome measure for life participation for use in all trials in kidney transplantation. Methods Twenty-five (43%) kidney transplant recipients/caregivers and 33 (57%) health professionals from eight countries participated. Transcripts were analyzed thematically. Results Four themes were identified. Returning to normality illustrated the patients’ desires to fulfil their given role and re-establish a normal lifestyle. Recognizing the diverse meaning of ‘life’ explicitly acknowledged life participation as a subjective outcome that may refer to different activities for different patients. Capturing fluctuations in issues post-transplant recognized the long-term impact of transplantation and emphasized the need to consider time since receiving the transplant. Having a scientifically rigorous, feasible and meaningful measure would facilitate the consistent and frequent assessment of life participation in trials. Conclusions A simple and inexpensive core outcome measure for life participation will allow this important outcome to be consistently and meaningfully assessed in trials in kidney transplantation to inform decision-making and care of patients
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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 | 0.207 | 0.272 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".