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Record W7115823877

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2021· dissertation· en· W7115823877 on OpenAlexaboutno aff

Bibliographic record

VenueMacSphere (McMaster University) · 2021
Typedissertation
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsRandomized controlled trialSystematic reviewQualitative researchEmergency departmentFocus groupPatient safetyResearch designClinical trial
DOInot available

Abstract

fetched live from OpenAlex

Background and Objectives \nOral anticoagulants (OACs) are among Canada's most frequently prescribed drugs and a top cause of medication-related serious harm leading to emergency department visits, hospitalizations, and fatalities. During the preparation to launch a Canadian Institutes of Health Research (CIHR)-funded randomized controlled trial (RCT) called "Improving Anticoagulant Safety at Hospital Discharge: A Randomized Trial," we faced some issues. First, as the RCT addresses OAC management, we needed to determine the barriers and facilitators for optimal OAC management, which were not identified in our literature search. Second, there is no core outcome set (COS) specific for OACs and the choice of outcomes and their measurement for the trial was not obvious. Finally, the drug-drug interactions between the OACs and other medications are not fully understood, particularly with regards to important clinical outcomes. Identifying the interacting medications and their interaction effect size, is vital to guarantee the safety of patients. To address these issues, the objectives of this thesis were: (1) to determine the barriers and facilitators for optimal OAC management, (2) to define the potential list for the COS of OACs, and (3) to explore the drug-drug interaction of OACs. \nMethods \nSeveral research approaches, including a systematic review, a systematic survey, a scoping review, a population-based retrospective cohort study with time varying methods, and a qualitative study were applied in this thesis. First, we applied both a synthesis review and qualitative research to explore the barriers and facilitators for OACs management to guarantee the evidence's robustness. Next, we used a systematic survey to address the lack of consensus on outcomes used and their \nv \ndefinitions for OAC treatment clinical trials. Finally, we used a systematic review and planned a population-based study to address drug-drug interaction related to OACs. \nMethodologic challenges and innovation \nIn the scoping review (Chapter 2: Barriers and facilitators to optimal oral anticoagulant management: a scoping review) and the focus group study (Chapter 3: Perceptions on patient education to improve oral anticoagulant management) we employed a qualitative approach. The main methodological challenge for both the scoping review and the focus group focused on the rigorous way to synthesize the themes. In Chapter 4, we used a systematic survey to explore the outcome list for OAC management research. The primary methodological challenge referred to the outcome reporting in the included studies. Not all outcomes performed in the trials can be reported for the space limitation or potential publication bias. In Chapters 5 and 6, a systematic review with meta-analysis and an observational protocol were used to explore the drug-drug interaction for OACs. The main methodological challenge for Chapter 5 was how to evaluate the drug-drug interaction (DDI) evidence systematically. The main methodological challenge for Chapter 6 is to address confounding and bias in a population-based protocol on DOACs drug-drug interaction. \nConclusion \nIn summary, this standard thesis describes five different background projects to prepare for an OAC management RCT. The papers contribute to the literature by using several research methodologies to provide useful evidence for OAC management and OAC research.

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)0.009
metaresearch head score (Gemma)0.051
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.442
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.051
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0040.002
Scholarly communication0.0070.005
Open science0.0030.006
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.5580.322

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 head0.032
GPT teacher head0.265
Teacher spread0.234 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2021
Admission routes1
Has abstractyes

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