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Record W2145805794 · doi:10.1016/j.farma.2009.08.002

Resistencia a la aspirina: prevalencia, mecanismos de acción y asociación con eventos tromboembólicos. Revisión narrativa

2010· review· es· W2145805794 on OpenAlexaff
Leticia Cañivano Petreñas, Cristina García Yubero

Bibliographic record

VenueFarmacia Hospitalaria · 2010
Typereview
Languagees
FieldMedicine
TopicAntiplatelet Therapy and Cardiovascular Diseases
Canadian institutionsSt. Thomas Hospital
Fundersnot available
KeywordsAspirinMedicinePlatelet aggregation inhibitorPlatelet aggregationEpidemiologyPopulationMEDLINEIntensive care medicineInternal medicinePlatelet

Abstract

fetched live from OpenAlex

Revisar la prevalencia de resistencia a la aspirina en pacientes de alto riesgo cardiovascular y, de forma secundaria, investigar la epidemiología, los mecanismos de acción de este fenómeno y las consecuencias clínicas que se derivan de ello. Se realizó una búsqueda en PubMed, EMBASE y Reviews Database de artículos publicados en inglés y en español hasta noviembre de 2008 referidos a resistencia a la aspirina. Se hizo también un seguimiento de las referencias para recuperar aquéllas consideradas relevantes para los objetivos secundarios de esta revisión. La resistencia a la aspirina se describe entre un 0 y un 57%, lo que se traduce en una disminución de la eficacia protectora frente a eventos cerebrovasculares y cardiovasculares. Numerosos factores modificables, así como otros no modificables, influyen en la eficacia del efecto antiagregante. Las estrategias por seguir para contrarrestar esta antiagregación disminuida pueden dirigirse a un aumento de dosis de aspirina o a la terapia dual junto con otros agentes antiplaquetarios. La falta de respuesta a la aspirina disminuye su eficacia protectora. Sin embargo, la falta de una definición de resistencia a la aspirina universalmente consensuada y la inexistencia de un método diagnóstico de referencia que identifique con fiabilidad a pacientes resistentes, así como los diferentes mecanismos de acción implicados en la agregación plaquetaria, cuestionan la relevancia clínica de este fenómeno. Son necesarios más estudios clínicos adecuadamente diseñados para detectar pacientes con verdadera resistencia a la aspirina y así conseguir prevenir de manera más eficaz la morbimortalidad cardiovascular. The purpose of this study is to review the prevalence of aspirin resistance in patients with a high risk of cardiovascular events, and secondly, to investigate its epidemiology and mechanism of action, and the clinical consequences it can provoke. A search was run on PubMed, EMBASE and Reviews Database for English or Spanish articles on aspirin resistance published up to November 2008. Additional studies were obtained by searching the reference lists in the selected articles for articles relevant to our secondary objectives. Aspirin resistance is described as affecting 0 to 57% of the population, and is related to a decreased protective effect against strokes and cardiovascular events. Many modifiable and unmodifiable factors can affect the efficacy of antiplatelet drugs. Possible strategies for overcoming this decreased antiaggregant effect include increasing the aspirin dosage or dual therapy with another antiplatelet agent. Lack of response to aspirin decreases its protective effects. However, lack of a standard definition for aspirin resistance, the absence of diagnostic reference methods to identify resistant patients, and the different mechanisms of action involved in platelet aggregation call the clinical importance of this fact into question. Additional well-designed studies are needed to detect patients with real resistance in order to have more effective prevention of cardiovascular morbidity and mortality.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0060.006
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.016
GPT teacher head0.318
Teacher spread0.302 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations6
Published2010
Admission routes1
Has abstractyes

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