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Low-molecular-weight heparins in ischemic heart disease

2004· review· en· W1990827669 on OpenAlexaff
Andrew T. Yan, Shaun G. Goodman

Bibliographic record

VenueCurrent Opinion in Cardiology · 2004
Typereview
Languageen
FieldMedicine
TopicAcute Myocardial Infarction Research
Canadian institutionsSt. Michael's HospitalUniversity of TorontoCanadian Heart Research Centre
Fundersnot available
KeywordsMedicineMyocardial infarctionLow molecular weight heparinCardiologyInternal medicineAnginaHeparinUnstable anginaFibrinolysisInfarctionFibrinolytic agentAntithrombotic

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: The objective of this review was to summarize the recent developments regarding the use of low-molecular-weight heparins in the management of acute coronary syndromes. RECENT FINDINGS: In the setting of unstable angina and non-ST-elevation myocardial infarction, enoxaparin is superior to unfractionated heparin in reducing death, myocardial infarction, and recurrent ischemia both in the short-term and to 1 year. However, this does not necessarily imply a class effect of low-molecular-weight heparins in general. When combined with glycoprotein IIb/IIIa inhibitors, enoxaparin appears to be effective and safe even for patients treated according to an early invasive strategy. In patients receiving fibrinolytics for ST-elevation myocardial infarction, low-molecular-weight heparins are as effective as unfractionated heparin in maintaining patency of the infarct-related artery and in reducing the composite endpoint of death and reinfarction. However, serious bleeding is more common, especially among the elderly, and the optimal dosing regimen in ST-elevation myocardial infarction remains to be defined. SUMMARY: Low-molecular-weight heparins are safe and effective in the management of unstable angina and non-ST-elevation myocardial infarction, with or without concurrent administration of glycoprotein IIb/IIIa inhibitors. Ongoing studies will clarify the role of low-molecular-weight heparins as adjunctive therapy for fibrinolysis.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.834
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.090
GPT teacher head0.439
Teacher spread0.348 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations7
Published2004
Admission routes1
Has abstractyes

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