Setting New Directions for Research in Childhood Nephrotic Syndrome: Results From a National Workshop
Bibliographic record
Abstract
BACKGROUND: We report on the proceedings of a national workshop held in Canada with the aims to identify priorities for research in childhood nephrotic syndrome and to develop a national strategy to address these priorities. METHODS: A diverse group of participants attended the meeting, including patients, family members, researchers, and health care providers. We used small group discussions to explore priorities as perceived by patients and families and by health care providers and researchers. RESULTS: Research evaluating glucocorticoid minimization or glucocorticoid-sparing regimens was a consistent theme in the patient and family discussion group. Families also indicated the need for precise prognostic information at diagnosis, more information to help them choose the best available therapy, and more resources for disease management. Health care providers emphasized the importance of better disease characterization including genotyping and phenotyping patients, better understanding the pathogenesis, and the need of providing targeted therapy and precise prognostic information. CONCLUSIONS: These priorities will inform the development and future directions of the Canadian Childhood Nephrotic Syndrome (CHILDNEPH) project, a national research initiative to improve care and outcomes of patients with childhood onset nephrotic syndrome.
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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 | 0.211 | 0.108 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.007 | 0.032 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 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".