Assessment of Pediatric Nephrology Programs’ Readiness to Participate in Prospective Clinical Trials
Bibliographic record
Abstract
Background: Currently, there is a limited availability of effective, FDA-approved drugs for children with kidney disease in the United States; therefore, there is an increased need for clinical trials to evaluate drug effectiveness and safety for pediatric usage. Implementation and conduction of clinical trials is complex process, which requires a team approach, involves multiple inter-dependent steps, research infrastructure support, legal policies/procedures, equipment, and access to a regulatory oversight board. The conduct of clinical trials in small pediatric subspecialties (i.e. pediatric nephrology) may be hampered by provider clinical demands and small numbers of patients available for such studies. The goal of this survey was to assess the readiness to conduct clinical trials by pediatric nephrologists in institutions of different sizes. Assessment would also give consideration to the sites for prospective trials and educate the programs about steps needed in clinical research. Methods: The survey was designed and tested by a small group of pediatric nephrology experts. Qualtrics Online Survey Platform and Statistical analysis were used. The survey was distributed to members of ASPN (60 sites in 30 U.S states and 2 Canadian Provinces). Respondents were asked to complete the survey on behalf of the institution/practice, not their individual preferences. There was a total of 17 survey questions, which assessed the respondent's institution's participation/interest in conduction of clinical research, availability of a clinical research coordinator/IRB, and access to equipment for trial execution (dry ice, centrifuges, freezers). Results: Currently, we have recorded 68 survey responses. Two of the responding institutions had no interest in conducting clinical trials (2.9%). Notably, more respondents practiced at Academic Centers/Universities (91%) than in private practices (8.3%). We noted no major differences in access to clinical trial resources between large and small institutions. Conclusions: Clinical trials remain vital to finding better treatments and cures for pediatric patients with renal diseases. Overall, pediatric nephrology programs have good infrastructure and readiness to conduct clinical trials independently of the size of the institution.
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 | 0.074 | 0.111 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".