(Optimizing Foundational Therapies in Patients With HFrEF. How Do We Translate These Findings Into Clinical Care? Translation of the document prepared by the Czech Society of Cardiology)
Bibliographic record
Abstract
Vzhledem k vysokĂŠmu riziku neŞådoucĂch ĂşÄinkĹŻ u pacientĹŻ se srdeÄnĂm selhĂĄnĂm a snĂĹženou ejekÄnĂ frakcĂ (heart failure and reduced ejection fraction, HFrEF) existuje nalĂŠhavĂĄ potĹeba zahĂĄjenĂ a titrace farmakoterapie podle doporuÄenĂ˝ch postupĹŻ (guideline-directed medical therapy, GDMT), kterĂĄ mĹŻĹže snĂĹžit morbiditu a riziko ĂşmrtĂ. DoporuÄenĂŠ postupy pro klinickou praxi nynĂ zdĹŻrazĹujĂ nutnost vÄasnĂŠho a rychlĂŠho zahĂĄjenĂ lĂŠÄby lĂĄtkami s kardiovaskulĂĄrnĂm pĹĂnosem. PĹi vÄdomĂ ÄetnĂ˝ch pĹekĂĄĹžek ztÄĹžujĂcĂch zahĂĄjenĂ a optimalizaci GDMT musĂ bĂ˝t cĂlem poskytovatelĹŻ zdravotnĂ pĂŠÄe uvĂĄdÄt do praxe ÄtyĹi pilĂĹe farmakoterapie kombinacĂ ÄtyĹ lĂŠkovĂ˝ch skupin, kterou dnes preferuje vÄtĹĄina doporuÄenĂ˝ch postupĹŻ pro klinickou praxi: inhibitory receptoru pro angiotenzin II a neprilysinu, beta-blokĂĄtory, antagonisty mineralokortikoidnĂch receptorĹŻ a inhibitory sodĂko-glukĂłzovĂŠho kotransportĂŠru 2. I kdyĹž u vysokĂŠho procenta pacientĹŻ s HFrEF nejsou pĹĂtomny ŞådnĂŠ klinickĂŠ kontraindikace GDMT, pĹesto jim tato lĂŠÄiva nejsou pĹedepisovĂĄna. VÄasnĂŠ zahĂĄjenĂ kombinaÄnĂ lĂŠÄby s nĂzkĂ˝mi dĂĄvkami by mÄla snĂĄĹĄet vÄtĹĄina pacientĹŻ. PĹi hledĂĄnĂ maximĂĄlnĂ tolerovanĂŠ GDMT bude nicmĂŠnÄ nutno zvaĹžovat i faktory na stranÄ pacientĹŻ, jako jsou jejich hemodynamickĂŠ pomÄry, kĹehkost a laboratornĂ hodnoty. DalĹĄĂ vĂ˝znamnou moĹžnost pro ĂşspÄĹĄnĂŠ provĂĄdÄnĂ GDMT pĹedstavuje zahĂĄjenĂ GDMT bÄhem hospitalizace pro akutnĂ srdeÄnĂ selhĂĄnĂ. Pro omezenĂ polypragmazie a snĂĹženĂ rizika neŞådoucĂch ĂşÄinkĹŻ lze koneÄnÄ zvaĹžovat i vysazenĂ lĂŠÄiv bez zjevnĂŠho pĹĂnosu pro kardiovaskulĂĄrnĂ systĂŠm. SnĂĹženĂ morbidity a mortality pacientĹŻ s HFrEF si vyŞådĂĄ dalĹĄĂ prospektivnĂ studie zamÄĹenĂŠ na optimĂĄlnĂ provĂĄdÄnĂ farmakoterapie ÄtyĹkombinacĂ. (J Am Coll Cardiol Basic Trans Science 2022;7:504-517) © 2021 The Authors. Published by Elsevier on behalf of the American College of Cardiology Foundation. Jde o ÄlĂĄnek vydanĂ˝ pod licencĂ CC BY-NC-ND (http://creativecommons.org/licenses/by-nc-nd/4.0/). ISSN 2452-302X, https://doi.org/10.1016/j.jacbts.2021.10.018
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".