Mitral regurgitation management: a systematic review of clinical practice guidelines and recommendations
Bibliographic record
Abstract
Multiple guidelines exist for the diagnosis and management of mitral regurgitation (MR), the second most common valvular heart disease in high-income countries, with recommendations that do not always match. We systematically reviewed guidelines on diagnosis and management of MR, highlighting similarities and differences to guide clinical decision-making. We searched national and international guidelines in MEDLINE and EMBASE (1 June 2010 to 1 September 2021), the Guidelines International Network, National Guideline Clearinghouse, National Library for Health Guidelines Finder, Canadian Medical Association Clinical Practice Guidelines Infobase, and websites of relevant organizations. Two reviewers independently screened the abstracts and identified articles of interest. Guidelines that were rigorously developed (as assessed with the Appraisal of Guidelines for Research and Evaluation II instrument) were retained for analysis. Five guidelines were retained. There was consensus on a multidisciplinary approach from the heart team and for the definition and grading of severe primary MR. There was general agreement on the thresholds for intervention in symptomatic and asymptomatic primary MR; however, discrepancies were present. There was agreement on optimization of medical therapy in severe secondary MR and intervention in patients symptomatic despite optimal medical therapy, but no consensus on the choice of intervention (surgical repair/replacement vs. transcatheter approach). Cut-offs for high-risk intervention in MR, risk stratification of progressive MR, and guidance on mixed valvular disease were sparse.
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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 | 0.019 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.019 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".