Use of ezetimibe results in more patients reaching lipid targets without side effects
Bibliographic record
Abstract
Ăvod: Mechanismus ĂşÄinku ezetimibu doplĹuje ĂşÄinek statinĹŻ, a tudĂĹž je ezetimib vhodnou lĂŠÄbou pro pacienty, kteĹĂ netolerujĂ hypolipidemika, nebo pro ty, kteĹĂ nedosahujĂ cĂlovĂ˝ch hodnot lipidĹŻ.CĂl: Zhodnotit ĂşÄinnost a bezpeÄnost ezetimibu pĹi dlouhodobĂŠm sledovĂĄnĂ pacientĹŻ v lipidologickĂŠ ambulanci s dĹŻrazem na motivaci k lĂŠÄbÄ a vliv na dosaĹženĂ cĂlovĂ˝ch hodnot lipidĹŻ.Metody: V naĹĄĂ databĂĄzi jsme identifikovali 295 ambulantnĂch pacientĹŻ, u kterĂ˝ch byla zahĂĄjena lĂŠÄba ezetimibem v dobÄ 13 mÄsĂcĹŻ po uvedenĂ tohoto lĂŠku na kanadskĂ˝ trh. AnamnestickĂĄ a laboratornĂ data byla shromaĹžÄovĂĄna pĹed zahĂĄjenĂm lĂŠÄby a pĹi prvnĂ nĂĄvĹĄtÄvÄ po zahĂĄjenĂ lĂŠÄby. Pro statistickĂŠ srovnĂĄnĂ ĂşÄinku ezetimibu na parametry lipidovĂŠho metabolismu byl pouĹžit pĂĄrovĂ˝ t-test a pro hodnocenĂ ĂşÄinku na dosaĹženĂ cĂlovĂ˝ch hodnot lipidĹŻ byl pouĹžit χ2 test.VĂ˝sledky: LĂŠÄba ezetimibem vedla ke zvýťenĂ podĂlu pacientĹŻ, kteĹĂ dosĂĄhli cĂlovĂŠ hodnoty lipidĹŻ (o 25 % pro LDL cholesterol a o 21,7 % pro pomÄr celkovĂŠho/HDL cholesterolu; p ≤ 0,001). Monoterapie ezetimibem vedla k vĂ˝znamnĂŠmu snĂĹženĂ LDL cholesterolu o 18 % (p < 0,001) a pomÄru celkovĂŠho/HDL cholesterolu o 15 % (p < 0,011). ĂÄinek ezetimibu kombinovanĂŠho s jinĂ˝mi hypolipidemiky byl obdobnĂ˝; LDL cholesterol se snĂĹžil o 22 % (p < 0,001) a pomÄr celkovĂŠho/HDL cholesterolu o 15,4 % (p < 0,011). StejnĂ˝ hypolipidemickĂ˝ ĂşÄinek byl zjiĹĄtÄn u diabetikĹŻ. V tĂŠto podskupinÄ nemocnĂ˝ch vedlo pĹidĂĄnĂ ezetimibu k dosavadnĂ lĂŠÄbÄ k trojnĂĄsobnĂŠmu nĂĄrĹŻstu pacientĹŻ, kteĹĂ dosĂĄhli cĂlovĂ˝ch hodnot LDL cholesterolu, a dvojnĂĄsobnĂŠmu nĂĄrĹŻstu poÄtu pacientĹŻ, kteĹĂ dosĂĄhli cĂlovĂŠho pomÄru celkovĂŠho/HDL cholesterolu. Pouze 7 % pacientĹŻ pĹeruĹĄilo lĂŠÄbu pro neŞådoucĂ ĂşÄinky.ZĂĄvÄr: U pacientĹŻ, kteĹĂ byli odeslĂĄni do specializovanĂŠho lipidologickĂŠho centra (typicky pro neŞådoucĂ ĂşÄinky hypolipidemik nebo nemoĹžnost dosĂĄhnout cĂlovĂ˝ch hodnot), vedla lĂŠÄba ezetimibem k vĂ˝znamnĂŠmu nĂĄrĹŻstu podĂlu tÄch, kteĹĂ dosĂĄhli cĂlovĂ˝ch hodnot lipidĹŻ. Tato lĂŠÄba nebyla provĂĄzena vĂ˝znamnĂ˝mi neŞådoucĂmi ĂşÄinky.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".