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Record W3006707881 · doi:10.1016/j.cjco.2020.02.005

Meta-analysis Comparing Outcomes of Type 2 Myocardial Infarction and Type 1 Myocardial Infarction With a Focus on Dual Antiplatelet Therapy

2020· article· en· W3006707881 on OpenAlexaff
Christopher M. Reid, Ahmed AlTurki, Andrew T. Yan, Derek So, Dennis T. Ko, Jean‐François Tanguay, Amal Bessissow, Shamir R. Mehta, Shaun G. Goodman, Thao Huynh

Bibliographic record

VenueCJC Open · 2020
Typearticle
Languageen
FieldMedicine
TopicAntiplatelet Therapy and Cardiovascular Diseases
Canadian institutionsMcMaster UniversityMontreal Heart InstituteUniversité de MontréalUniversity of OttawaSt. Michael's HospitalMcGill UniversitySunnybrook HospitalHamilton Health SciencesUniversity of Toronto
FundersAbbott VascularBoston Scientific CorporationMedtronicBayerAstraZenecaEli Lilly and CompanyServierAmgenSanofiMerckAbbott Laboratories
KeywordsMyocardial infarctionMedicineCardiologyInternal medicineDual (grammatical number)

Abstract

fetched live from OpenAlex

BACKGROUND: There are important knowledge gaps in type 2 myocardial infarction (T2MI). Our primary objective was to compare the outcomes of patients with T2MI with those of patients with type 1 myocardial infarction (T1MI). Our secondary objective was to determine whether randomized controlled trials (RCTs) evaluating dual antiplatelets (DAPTs) have explicitly included patients with T2MI. METHODS: We performed a meta-analysis comparing outcomes of patients with T2MI with patients with T1MI and a separate systematic review to evaluate the inclusion of T2MI in RCTs evaluating DAPT. There were 19 cohorts enrolling 48,829 patients (40,604 with T1MI and 5361 with T2MI) and 51 RCTs enrolling 188,132 patients with acute coronary syndrome. RESULTS: 0.0001, respectively). There was no published evaluation of efficacy, effectiveness, and safety of DAPT in patients with T2MI. CONCLUSION: Patients with T2MI are at increased risk of adjusted all-cause long-term mortality compared with patients with T1MI. The role of DAPT remains unclear in T2MI.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.443
Threshold uncertainty score0.673

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.087
GPT teacher head0.309
Teacher spread0.222 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations11
Published2020
Admission routes1
Has abstractyes

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