ISPD Cardiovascular and Metabolic Guidelines in Adult Peritoneal Dialysis Patients Part I – Assessment and Management of Various Cardiovascular Risk Factors
Bibliographic record
Abstract
Cardiovascular disease contributes significantly to the adverse clinical outcomes of peritoneal dialysis (PD) patients. Numerous cardiovascular risk factors play important roles in the development of various cardiovascular complications. Of these, loss of residual renal function is regarded as one of the key cardiovascular risk factors and is associated with an increased mortality and cardiovascular death. It is also recognized that PD solutions may incur significant adverse metabolic effects in PD patients. The International Society for Peritoneal Dialysis (ISPD) commissioned a global workgroup in 2012 to formulate a series of recommendations regarding lifestyle modification, assessment and management of various cardiovascular risk factors, as well as management of the various cardiovascular complications including coronary artery disease, heart failure, arrhythmia (specifically atrial fibrillation), cerebrovascular disease, peripheral arterial disease and sudden cardiac death, to be published in 2 guideline documents. This publication forms the first part of the guideline documents and includes recommendations on assessment and management of various cardiovascular risk factors. The documents are intended to serve as a global clinical practice guideline for clinicians who look after PD patients. The ISPD workgroup also identifies areas where evidence is lacking and further research is needed.
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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) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".