Annals Consult Guys - Could This Patient Have Heyde Syndrome?
Bibliographic record
Abstract
Web ExclusivesMarch 2023Annals Consult Guys - Could This Patient Have Heyde Syndrome?FREEHoward H. Weitz, MD, Geno J. Merli, MD, Theodore Earl Warkentin, MD, and Nicholas J. Ruggiero II, MDHoward H. Weitz, MDThomas Jefferson University, Philadelphia, Pennsylvania (H.H.W., G.J.M., N.J.R.), Geno J. Merli, MDThomas Jefferson University, Philadelphia, Pennsylvania (H.H.W., G.J.M., N.J.R.), Theodore Earl Warkentin, MDMcMaster University, Hamilton, Ontario, Canada (T.E.W.), and Nicholas J. Ruggiero II, MDThomas Jefferson University, Philadelphia, Pennsylvania (H.H.W., G.J.M., N.J.R.)Author, Article, and Disclosure Informationhttps://doi.org/10.7326/W22-0016 CME/MOC SectionsSupplemental MaterialAboutVisual Abstract ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail What is Heyde syndrome? In this episode, the Annals Consult Guys and their guests describe the syndrome and current thinking about appropriate management of patients with gastrointestinal bleeding associated with it.For more videos from and information on Annals Consult Guys, visit go.annals.org/ConsultGuys. Comments0 CommentsSign In to Submit A Comment Guy E. De GentOrion Cardiology, PLLC Greensboro, NC31 March 2023 Comment on Could This Patient Have Heyde Syndrome? Acquired VWD and GI bleeding is also seen with severe mitral regurgitation. It seems less likely that a MitraClip procedure would solve the GI bleeding. There is volumetric less MR, but now you have two smaller orifices, probably with higher shear stress. Do we have data? Is there even less improvement then 50% post TAVR GI bleeding improvement? Author, Article, and Disclosure InformationAuthors: Howard H. Weitz, MD; Geno J. Merli, MD; Theodore Earl Warkentin, MD; Nicholas J. RuggieroII, MDAffiliations: Thomas Jefferson University, Philadelphia, Pennsylvania (H.H.W., G.J.M., N.J.R.)McMaster University, Hamilton, Ontario, Canada (T.E.W.)Disclosures: Drs. Merli and Weitz report that they have no financial relationships or interests to disclose. Dr. Warkentin reports grants or contracts from Werfen; royalties or licenses from Informa and Wolters Kluwer; consulting fees from Aspen Canada/Aspen Global, CSL Behring, Ergomed, Paradigm Pharmaceuticals, Octapharma, Veralox Therapeutics, and Werfen; payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing, or educational events from Werfen; payment for expert testimony from Medical-Legal; and participation on a data safety monitoring or advisory board from Octapharma and Paradigm Pharmaceuticals. Dr. Ruggiero reports grants or contracts for the Bard True Balloon Study and the Ancora Heart Corcinch Trial, and payment for expert testimony from the U.S. government and Post & Schell. All relevant financial relationships have been mitigated. Forms can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=W22-0016.Editors' Disclosures: The editors have no relevant financial relationships to disclose. Individual forms may be viewed at www.annals.org/editorsdisclosures.Correction: The CME/MOC activity for this article was corrected on 3 April 2023 to correct the answer to question 1. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetails Metrics March 2023Volume 176, Issue 3 ePublished: 21 March 2023 Issue Published: March 2023 Copyright & PermissionsCopyright © 2023 by American College of Physicians. All Rights Reserved.Loading ...
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.214 | 0.050 |
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".