ISN Nexus 2016 Symposia: Translational Immunology in Kidney Disease—The Berlin Roadmap
Bibliographic record
Abstract
To date, the treatment of immune-mediated kidney diseases has only marginally benefited from highly specific biological drugs that have demonstrated remarkable effects in many other diseases. What accounts for this disparity? In April 2016, the International Society of Nephrology held a Nexus meeting on Translational Immunology in Nephrology in Berlin, Germany, to identify and discuss hurdles that block the translational flow of target identification, and preclinical and clinical target validation in the domain of immune-mediated kidney disease. A broad panel of experts including basic scientists, translational researchers, clinical trialists, pharmaceutical industry drug developers, and representatives of the American and European regulatory authorities made recommendations on how to overcome such hurdles at all levels of the translational research process. The results of these discussions are presented here, which may serve as a roadmap for how to optimize the process of developing more innovative and effective drugs for patients with immune-mediated kidney diseases.
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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 | 0.015 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.031 | 0.013 |
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".