Cardio-renal syndromes: report from the consensus conference of the Acute Dialysis Quality Initiative
Bibliographic record
Abstract
A consensus conference on cardio-renal syndromes (CRS) was held in Venice Italy, in September 2008 under the auspices of the Acute Dialysis Quality Initiative (ADQI). The following topics were matter of discussion after a systematic literature review and the appraisal of the best available evidence: definition/classification system; epidemiology; diagnostic criteria and biomarkers; prevention/protection strategies; management and therapy. The umbrella term CRS was used to identify a disorder of the heart and kidneys whereby acute or chronic dysfunction in one organ may induce acute or chronic dysfunction in the other organ. Different syndromes were identified and classified into five subtypes. Acute CRS (type 1): acute worsening of heart function (AHF-ACS) leading to kidney injury and/or dysfunction. Chronic cardio-renal syndrome (type 2): chronic abnormalities in heart function (CHF-CHD) leading to kidney injury and/or dysfunction. Acute reno-cardiac syndrome (type 3): acute worsening of kidney function (AKI) leading to heart injury and/or dysfunction. Chronic reno-cardiac syndrome (type 4): chronic kidney disease leading to heart injury, disease, and/or dysfunction. Secondary CRS (type 5): systemic conditions leading to simultaneous injury and/or dysfunction of heart and kidney. Consensus statements concerning epidemiology, diagnosis, prevention, and management strategies are discussed in the paper for each of the syndromes.
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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 | 0.024 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".