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Record W4284879511 · doi:10.1101/2022.07.05.22277267

Decisional Needs and Patient Treatment Preferences for Heart Failure Medications: Scoping Review Protocol

2022· preprint· en· W4284879511 on OpenAlexaffabout
Ricky D. Turgeon, Arden R. Barry, Blair J. MacDonald

Bibliographic record

VenuemedRxiv · 2022
Typepreprint
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsSt. Paul's HospitalNative Mental Health Association of CanadaUniversity of British Columbia
Fundersnot available
KeywordsCINAHLDecision aidsMEDLINEData extractionInclusion (mineral)Psychological interventionProtocol (science)UploadInclusion and exclusion criteriaSystematic reviewMedicinePsychologyComputer scienceAlternative medicineNursingWorld Wide WebSocial psychologyPolitical science

Abstract

fetched live from OpenAlex

Abstract Introduction Treatment decisions regarding heart failure with reduced ejection fraction (HFrEF; ejection fraction ≤40%) pharmacotherapy are complex. Decision aids can bridge this knowledge-to-practice gap and improve the integration of patients’ preferences and values for patient-centered care. However, little is known about the preferences and decisional needs of patients regarding these medications. The objectives of this scoping review are to identify, map and synthesize the literature evaluating the decisional needs, treatment preferences and values of patients making decisions regarding HFrEF medications. Methods and Analysis We will search MEDLINE, Embase, CINAHL (inception-April 2022), bibliographies of included studies and relevant reviews, Web of Science ‘cited references’, cocites.com , clinicaltrials.gov , Epistemonikos, and the Ottawa Decision Aid Inventory, without language restriction. We will include qualitative, quantitative, and mixed-methods studies that describe patient and clinician decisional needs, or patient treatment preferences or values regarding HFrEF medications guided by the Ottawa Decision Support Framework, or decision aids to support HFrEF medication decisions. One author will perform all searches and upload results to Covidence. Two review authors will independently screen retrieved article titles and abstracts for inclusion, review full-text for final inclusion, and extract data from included articles and decision aids using a standardized data extraction form. We will present a graphical abstract mapping what is known about decisional needs and patient preferences and values, decisional support interventions, and decisional outcomes regarding HFrEF medications. We will also describe extracted data in narrative and tabular format to address the scoping review objectives, and discuss implications for practice and subsequent research in the field of shared decision-making for HFrEF. Ethics and Dissemination Research ethics board approval is not required for this scoping review of published data. We will present the findings at relevant conferences, publish a peer-reviewed manuscript, and disseminate results via institutional and partner social media platforms. Strengths and limitations of this study This scoping review will systematically identify, map and synthesize the decisional needs, treatment preferences and values of patients regarding heart failure medication decisions. We have developed a comprehensive search strategy to identify qualitative, quantitative and mixed-methods studies (published and unpublished), as well as decision aids. The review will follow methodology outlined in the Joanna Briggs Institute Manual for Evidence Synthesis and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews reporting guidelines. The results of the scoping review will ultimately be limited by available studies; however, preliminary searches have identified several eligible studies.

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)0.076
metaresearch head score (Gemma)0.083
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: Systematic review
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.097
Threshold uncertainty score0.400

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0760.083
Meta-epidemiology (narrow)0.0050.006
Meta-epidemiology (broad)0.0130.013
Bibliometrics0.0210.017
Science and technology studies0.0050.005
Scholarly communication0.0090.010
Open science0.0060.008
Research integrity0.0080.005
Insufficient payload (model declined to judge)0.0970.014

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 head0.063
GPT teacher head0.384
Teacher spread0.321 · 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
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

Citations1
Published2022
Admission routes2
Has abstractyes

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