Focused Update of 2016 Korean Society of Heart Failure Guidelines for the Management of Chronic Heart Failure
Bibliographic record
Abstract
The incidence of heart failure (HF) is increasing in South Korea and devising the optimal care plan is crucial to promote appropriate and effective treatment of HF. To this end, the first Korean guideline for management of chronic HF was published in 2016 by the HF research group under the Korean Society of Cardiology (KSC). After this publication, considerable data have been accumulated and a new guideline for the management of HF was published in Europe, and an update regarding pharmacological therapy was published in the United States, which made it warrant to update the Korean guideline. Under this premise, The Clinical Practice Guidelines Committee under the Korean Society of Heart Failure (KSHF) founded in 2018 decided to publish a focused update management guideline for chronic HF and selected 15 topics that need an update regarding the diagnosis, definition, diagnostic algorithm, monitoring, novel biomarkers, drug therapy related to non-vitamin K antagonist oral anticoagulants and angiotensin receptor neprilysin inhibitors, and with respect to comorbidities changes in the guidelines of relevant institutions, such as new guidelines for the management of hypertension, a procedure used to treat severe aortic stenosis, information on sodium glucose co-transporter-2 inhibitor, and that about sleep apnea. Among nonpharmacological therapies, changes to the recommendations for implantable cardioverter defibrillator, cardiac resynchronization therapy, and cardiac rehabilitation were updated. Subsequent and continuous updates based on additional clinical research findings, with continual supervision by the KSHF will be 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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".