Patient Recruitment into a Multicenter Randomized Clinical Trial for Kidney Disease: Report of the Focal Segmental Glomerulosclerosis Clinical Trial (FSGS CT)
Bibliographic record
Abstract
We describe the experience of the focal segmental glomerulosclerosis clinical trial (FSGS CT) in the identification and recruitment of participants into the study. This National Institutes of Health funded study, a multicenter, open-label, randomized comparison of cyclosporine versus oral dexamethasone pulses plus mycophenolate mofetil, experienced difficulty and delays meeting enrollment goals. These problems occurred despite the support of patient advocacy groups and aggressive recruitment strategies. Multiple barriers were identified including: (1) inaccurate estimates of the number of potential incident FSGS patients at participating centers; (2) delays in securing one of the test agents; (3) prolonged time between IRB approval and execution of a subcontract (mean 7.5 ± 0.8 months); (4) prolonged time between IRB approval and enrollment of the first patient at participating sites (mean 19.6 ± 1.4 months); and (5) reorganization of clinical coordinating core infrastructure to align resources with enrollment. A Web-based anonymous survey of site investigators revealed site-related barriers to patient recruitment. The value of a variety of recruitment tools was of marginal utility in facilitating patient enrollment. We conclude that improvements in the logistics of study approval and regulatory start-up and testing of promising novel agents are important factors in promoting enrollment into randomized clinical trials in nephrology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.241 | 0.211 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".