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Record W4406147171 · doi:10.1017/s0266462324001405

OD02 Developing Components For A National Strategy For Heart Valve Disease In Canada

2024· article· en· W4406147171 on OpenAlexaboutno aff
Lindsey M. Warkentin, David Messika–Zeitoun

Bibliographic record

VenueInternational Journal of Technology Assessment in Health Care · 2024
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsHeart valveMedicineCardiologyHeart diseaseInternal medicineIntensive care medicine

Abstract

fetched live from OpenAlex

Introduction The research included a rapid review of current literature to describe epidemiology, management, and system impact of heart valve disease (HVD) in adult populations. Key issues were identified in consultation with expert focus groups and were framed across the continuum of care and systemic policy issues. The groupings were adapted and adjusted during deliberations. Methods A rapid literature review was conducted on HVD key interventions and evidence of effectiveness along with expert interviews to identify high-level themes for reform. This served as the evidentiary backgrounder. Two virtual policy engagements with clinical leaders, patients, and health system managers were conducted. The focus was their collective drafting of recommendations. These workshops identified nine thematic areas and developed associated recommendations for action under each theme. The success of the process is evident as the report has been taken up as a roadmap for ongoing research and policy work. Results A comprehensive grouping of recommendations for improving HVD detection, management, and treatment in Canada was produced. It was designed to be comprehensive to then allow more targeted work to proceed under an evidence-informed and clinically endorsed agenda. Conclusions Heart valve conditions are increasingly treatable, especially if detected early. Innovation in treatments as well as detection and management to address gaps in care were identified as the most urgent priorities. The key result was formation of formal working groups with a professional society to explore spoke–hub-and node models for care delivery and to launch awareness programs for early detection.

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)0.012
metaresearch head score (Gemma)0.019
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.832
Threshold uncertainty score0.965

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.019
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.003
Science and technology studies0.0070.002
Scholarly communication0.0070.002
Open science0.0030.005
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0120.001

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 head0.034
GPT teacher head0.453
Teacher spread0.420 · 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
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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Technology Assessment in Health CareSame topicCardiac Valve Diseases and TreatmentsFrench-language works237,207