Bibliographic record
Abstract
Estimating the prevalence of chronic kidney disease (CKD) is no simple task. The overall prevalence is relatively low but may be higher in select populations that are not accessible to surveys (e.g., certain ethnic groups, the sick or elderly). Moreover, the tests that define CKD lack precision and transportability to healthy populations. During the past decade, it is not clear that CKD has grown substantially. Some epidemiologic factors that are associated with CKD (obesity and diabetes) are increasing, whereas others (uncontrolled hypertension and smoking) are decreasing. Reasons for the discrepancy between a stable CKD population and ongoing ESRD growth remain speculative. There is evidence that ESRD rates may be stabilizing and that efforts to reduce progression in high-risk groups may be starting to show benefit. Expanding the definition of CKD and increasing detection may be required to reduce overall ESRD prevalence. One concern is that many of the well-defined high-risk patient groups (diabetes and black) are still undertreated. Increasing the investigation and treatment of low-risk patients may not be the answer. Clinical inertia (failure to initiate or change therapy) may be a more significant and modifiable barrier toward reducing ESRD, and this deserves increased attention. Furthermore, reducing CKD prevalence will require controlling the precipitating causes. The incremental benefit of detecting CKD in low-risk patients, use of expensive therapies in CKD, or new strategies such as the treatment of prehypertension require solid evidence, not only of the variety that shows benefit (hard end points) but also to whom, when, and at what cost.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.044 | 0.014 |
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".