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Record W2795465954 · doi:10.1136/bmjopen-2017-019555

Antithrombotic therapy in patients receiving saphenous vein coronary artery bypass grafts: a protocol for a systematic review and network meta-analysis

2018· review· en· W2795465954 on OpenAlexaff
Karla Solo, Janet Martin, Shahar Lavi, Conrad Kabali, Ava John‐Baptiste, Immaculate Nevis, Tawfiq Choudhury, Mamas A. Mamas, Rodrigo Bagur

Bibliographic record

VenueBMJ Open · 2018
Typereview
Languageen
FieldMedicine
TopicCoronary Interventions and Diagnostics
Canadian institutionsPublic Health OntarioUniversity of TorontoLondon Health Sciences CentreImpactWestern University
Fundersnot available
KeywordsMedicineAntithromboticMeta-analysisObservational studyRandomized controlled trialData extractionFibrinolytic agentIntensive care medicineInternal medicineMEDLINESurgery

Abstract

fetched live from OpenAlex

INTRODUCTION: The current evidence for the prevention of saphenous vein graft failure (SVGF) after coronary artery bypass graft (CABG) surgery consists of direct head-to-head comparison of treatments (including placebo) in randomised-controlled trials (RCTs) and observational studies. However, summarising the evidence using traditional pairwise meta-analyses does not allow the inclusion of data from treatments that have not been compared head to head. Exclusion of such comparisons could impact the precision of pooled estimates in a meta-analysis. Hence, to address the challenge of whether aspirin alone or in addition to another antithrombotic agent is a more effective regimen to improve SVG patency, a network meta-analysis (NMA) is necessary. The objectives of this study are to synthesise the available evidence on antithrombotic agents (or their combination) and estimate the treatment effects among direct and indirect treatment comparisons on SVGF and major adverse cardiovascular events, and to generate a treatment ranking according to their efficacy and safety outcomes. METHODS: We will perform a systematic review of RCTs evaluating antithrombotic agents in patients undergoing CABG. A comprehensive English literature search will be conducted using electronic databases and grey literature resources to identify published and unpublished articles. Two individuals will independently and in duplicate screen potential studies, assess the eligibility of potential studies and extract data. Risk of bias and quality of evidence will also be evaluated independently and in duplicate. We will investigate the data to ensure its suitability for NMA, including adequacy of the outcome data and transitivity of treatment effects. We plan to estimate the pooled direct, indirect and the mixed effects for all antithrombotic agents using a NMA. ETHICS AND DISSEMINATION: Due to the nature of the study, there are no ethical concerns nor informed consent required. We anticipate that this NMA will be the first to simultaneously assess the relative effects of multiple antithrombotic agents in patients undergoing CABG. The results of this NMA will inform clinicians, patients and guideline developers the best available evidence on comparative effects benefits of antithrombotic agents after CABG while considering the side effect profile to support future clinical decision-making. We will disseminate the results of our systematic review and NMA through a peer-reviewed journal. PROSPERO REGISTRATION NUMBER: CRD42017065678.

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)0.085
metaresearch head score (Gemma)0.119
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.085
Threshold uncertainty score0.452

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0850.119
Meta-epidemiology (narrow)0.0060.004
Meta-epidemiology (broad)0.0180.027
Bibliometrics0.0110.011
Science and technology studies0.0030.003
Scholarly communication0.0070.005
Open science0.0060.004
Research integrity0.0070.006
Insufficient payload (model declined to judge)0.0400.005

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 head0.286
GPT teacher head0.495
Teacher spread0.208 · 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 designNot applicable
Domainnot available
GenreProtocol

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

Citations2
Published2018
Admission routes1
Has abstractyes

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