Novel therapies for resistant focal segmental glomerulosclerosis (FONT) phase II clinical trial: study design
Bibliographic record
Abstract
BACKGROUND: The lack of adequate randomized clinical trials (RCT) has hindered identification of new therapies that are safe and effective for patients with primary focal segmental glomerulosclerosis (FSGS), especially in patients who fail to respond to corticosteroids and immunosuppressive therapies. Recent basic science advances have led to development of alternative treatments that specifically target aberrant pathways of fibrosis which are relevant to disease progression in FSGS. There is a need for a flexible Phase II study design which will test such novel antifibrotic strategies in order to identify agents suitable for phase III testing. METHODS/DESIGN: The Novel Therapies for Resistant Focal Segmental Glomerulosclerosis (FONT) project is a multicenter Phase I/II RCT designed to investigate the potential efficacy of novel therapies for resistant FSGS. Adalimumab and galactose will be evaluated against conservative therapy consisting of the combination of lisinopril, losartan and atorvastatin. The sample size is defined to assure that if one of the treatments has a superior response rate compared to that of the other treatments, it will be selected with high probability for further evaluation. Comparison of primary and secondary endpoints in each study arm will enable a choice to be made of which treatments are worthy of further study in future Phase III RCT. DISCUSSION: This report highlights the key features of the FONT II RCT including the two-step outcome analysis that will expedite achievement of the study objectives. The proposed phase II study design will help to identify promising agents for further testing while excluding ineffective agents. This staged approach can help to prevent large expenditures on unworthy therapeutic agents in the management of serious but rare 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 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".