Special Article: The Study of Treatment for Renal Insufficiency: Data and Evaluation (STRIDE), a National Registry of Chronic Kidney Disease
Bibliographic record
Abstract
Optimization of care in patients with chronic kidney disease (CKD) could be the key to improved clinical and economic outcomes, both during the phase of CKD as well as in patients with end-stage renal disease (ESRD). CKD is a major public health problem that has been insufficiently studied. There is little published information on outcomes among CKD patients, specifically, data on mortality, morbidity, and quality of life. Indeed, recent efforts by the National Kidney Foundation (NKF) have served to define the classification, evaluation, and approach to management of CKD in practice. The Study of Treatment for Renal Insufficiency: Data and Evaluation (STRIDE) registry is an initiative to study CKD patients in nephrology practices across the country. It is a prospective observational study whose objective is to profile demographic and clinical variables, practice patterns, comorbid conditions, quality of life, and outcomes in a nationally based sample of CKD patients. This article details the design, methodology, and process of enrollment into the registry.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".