ЗАБОЛЕВАНИЯ ПОЧЕК: БРЕМЯ БОЛЕЗНИ И ДОСТУПНОСТЬ МЕДИЦИНСКОЙ ПОМОЩИ
Bibliographic record
Abstract
Racial/ethnic and socioeconomic disparities in chronic kidney disease (CKD) have been documented for decades, yet little progress has been made in mitigating them. Several recent studies offer new insights into the root causes of these disparities, point to areas where future research is warranted and identify opportunities for changes in policy and clinical practice. Recently published evidence suggests that geographic disparities in CKD prevalence exist and vary by race. CKD progression is more rapid for racial/ethnic minority groups as compared to whites and may be largely, but not completely, explained by genetic factors. Stark socioeconomic disparities in outcomes for dialysis patients exist, and vary by race, place of residence and treatment facility. Disparities in access to living kidney donation may be driven primarily by the socioeconomic status of the donor as opposed to recipient factors. Recent studies highlight opportunities to eliminate disparities in CKD, including efforts to direct resources to areas and populations where disparities are most prevalent, efforts to understand how to best use emerging information on the contribution of genetic factors to disparities, and continued work to identify modifiable environmental, social, and behavioral factors for targeted interventions among high-risk populations.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.009 |
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".