Implementing core outcomes in kidney disease: report of the Standardized Outcomes in Nephrology (SONG) implementation workshop
Bibliographic record
Abstract
There are an estimated 14,000 randomized trials published in chronic kidney disease. The most frequently reported outcomes are biochemical endpoints, rather than clinical and patient-reported outcomes including cardiovascular disease, mortality, and quality of life. While many trials have focused on optimizing kidney health, the heterogeneity and uncertain relevance of outcomes reported across trials may limit their policy and practice impact. The international Standardized Outcomes in Nephrology (SONG) Initiative was formed to identify core outcomes that are critically important to patients and health professionals, to be reported consistently across trials. We convened a SONG Implementation Workshop to discuss the implementation of core outcomes. Eighty-two patients/caregivers and health professionals participated in plenary and breakout discussions. In this report, we summarize the findings of the workshop in two main themes: socializing the concept of core outcomes, and demonstrating feasibility and usability. We outline implementation strategies and pathways to be established through partnership with stakeholders, which may bolster acceptance and reporting of core outcomes in trials, and encourage their use by end-users such as guideline producers and policymakers to help improve patient-important 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 | 0.250 | 0.256 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.006 | 0.011 |
| 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".