Identification of patients and risk factors in chronic kidney disease--evaluating risk factors and therapeutic strategies
Bibliographic record
Abstract
Three strategies can help delay chronic kidney disease (CKD) progression: early identification of patients, modification of risk factors, and implementation of the best interventions. Early identification of patients requires accurate screening tools. As serum creatinine is an unreliable marker of kidney dysfunction, clinicians should focus on the glomerular filtration rate or other markers of true kidney function. Clinicians should also be aware of other indicators of abnormal kidney function, such as anaemia, acidosis, and increases in parathyroid hormone level. Additionally, both clinicians and patients should be aware of the "non-modifiable" and "modifiable" risk factors for CKD. Non-modifiable risk factors include age, gender, race, diabetes, and genetic make-up, while modifiable risk factors include elevated blood pressure and blood glucose, proteinuria, anaemia, metabolic disturbances, and dyslipidaemia. Patients should be particularly aware of the risk factors common to both cardiac and kidney disease, such as hypertension, proteinuria, anaemia, and (possibly) dyslipidaemia and diabetes. A single centre study demonstrated that inclusion in a multidisciplinary CKD clinic programme produced the greatest increases in time to renal replacement therapy, haemoglobin levels, and epoetin treatment usage at initiation of dialysis in comparison with standard nephrology care or no care. Two years after starting dialysis, the number of deaths was lowest, and the number of patients who had received a transplant or were still on dialysis was highest, in the CKD clinic-treated group. These results confirm those of previous studies, which showed that timely referral to a multidisciplinary team for management prior to dialysis decreases the risk of adverse patient outcomes. This suggests that a multidisciplinary, collaborative, proactive approach increases the likelihood of early identification of CKD patients and risk factor modification. However, further evidence-based demonstrations of success are required, showing benefit to both patients and health care systems.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".