Preditores Clínicos e Laboratoriais do Desenvolvimento de Valvopatias na Doença Renal Crônica: Uma Revisão Sistemática
Bibliographic record
Abstract
BACKGROUND: Chronic kidney disease (CKD) is associated with a higher prevalence of valvular diseases and increased mortality from cardiovascular causes. Factors that influence the genesis of cardiac valve calcification (CVC) in these patients are not well-defined. OBJECTIVE: To determine the risk factors for valvular calcification in patients with CKD. METHODS: Systematic review based on PRISMA, which included observational studies evaluating the association of clinical and laboratory data with CVC in patients with CKD, undergoing or not hemodialysis or peritoneal dialysis. Articles were retrieved from databases (MEDLINE; SCIELO; CENTRAL; EMBASE; LILACS/BVS) and selected blindly by two authors; discrepancies were resolved by a third author. Data collection and synthesis were carried out by the main author. The assessment of methodological quality and risk of bias was based on STROBE and Newcastle-Ottawa guidelines. RESULTS: A total of 783 studies were identified, of which 20 were included, encompassing 13,314 patients from 10 countries. The factors most strongly associated with CVC were age >55 years, glomerular filtration rate <53 mL/min/1.73m2, renal replacement therapy (RRT) >20 months, hypoalbuminemia, C-reactive protein (CRP), serum levels of IL-6, TNF-α, parathyroid hormone, hyperphosphatemia, hypercalcemia, Ca × P product, and FGF-23 resulting from secondary hyperparathyroidism. Both mitral and aortic valves were studied, and no differences were observed between hemodialysis and peritoneal dialysis. CONCLUSION: Age, RRT, chronic inflammation, and secondary hyperparathyroidism promote calcium and phosphate deposition in the valves, making CKD patients more susceptible to CVC) Monitoring these parameters provides opportunities for prevention and treatment.
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 | 0.031 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".