Definition and classification of Cardio-Renal Syndromes: workgroup statements from the 7th ADQI Consensus Conference
Bibliographic record
Abstract
Both cardiac and renal diseases are extremely common in the population and frequently coexist. Cardiac disease is often associated with worsening renal function and vice versa. The coexistence of cardiac and renal disease significantly increases mortality, morbidity and complexity and cost of care [1,2]. Syndromes describing the interaction between the heart and the kidney are recognized, but have never been clearly defined and classified. Several different definitions have been proposed [1,3–8] but none have been published as a result of a consensus process. As a result of the lack of consensus definition and classification, there is limited appreciation of its epidemiology, no standardized diagnostic criteria and no uniform approaches to prevention and treatment. Furthermore, treatment is often fragmented, single organ centred, with perceived competing priorities and specialty care is not necessarily integrated amongst relevant specialties. As a result, timing and appropriateness of care may suffer. In response to these issues, a consensus conference was organized under the auspices of the Acute Dialysis Quality Initiative (ADQI) by bringing together key opinion leaders and experts in the fields of nephrology, critical care, cardiac surgery, cardiology and epidemiology. A meeting was held in Venice, Italy from September 3 to 6, 2008. In this manuscript, we present the consensus document and the methodology by which a consensus definition and classification system for Cardio-Renal Syndromes was reached [9].
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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 | 0.047 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".