Bibliographic record
Abstract
PURPOSE OF REVIEW: Chronic kidney disease (CKD) is a pervasive and growing health concern that has a significant impact on mortality and morbidity, putting stress on global healthcare systems. CKD affects ∼14% of general populations and ∼36% of high-risk populations and is projected to rise in the coming decade due to increasing rates of diabetes and hypertension. RECENT FINDINGS: Screen, triage, and treat programs aim to detect early stage disease with the intention of promoting medical and lifestyle interventions in line with a patient's level of risk that may slow disease progression and reduce morbidity and mortality. Early detection facilitates appropriate risk stratification and coordination of care among patients, primary care and nephrology ensuring resources are utilized appropriately. SUMMARY: By using readily available laboratory measures, screening for CKD in high-risk populations is cost effective and beneficial to both individuals and healthcare systems. Program models such as Kidney Early Evaluation Program and First Nations Community Based Screening to Improve Kidney Health and Prevent Dialysis have proven the efficacy of screening initiatives in these groups, but improvements are required to maximize the benefits of early CKD detection.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".