Chronic kidney disease: why is current management uncoordinated and suboptimal?
Bibliographic record
Abstract
Morbidity and mortality associated with chronic kidney disease (CKD) is higher than that of the normal population, and the incidence of end-stage renal disease (ESRD) continues to increase. Several factors contribute to the uncoordinated and suboptimal management of CKD, including the attitude and behaviour of nephrologists, referring physicians and patients, and economic constraints on healthcare systems. Late referral of at-risk patients to specialist care is an area of particular concern, as this denies nephrologists adequate opportunity to prevent progression of CKD and associated complications such as anaemia. Due to the ageing population and advances in technology, the costs of treating CKD and ESRD continue to escalate and represent another barrier to the delivery of optimal care. Optimizing the care provided to CKD patients requires a coordinated approach to the management of the condition. Closer collaboration and improved communication across specialities is important for the timely referral of patients and for efficient utilization of available resources. A multidisciplinary approach may facilitate patient identification and improve the management of CKD.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".