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Record W3199665368 · doi:10.1016/j.cjco.2021.09.004

Racial and Ethnic Differences in the Management of Atrial Fibrillation

2021· review· en· W3199665368 on OpenAlexaff
Kamala P. Tamirisa, Sana M. Al‐Khatib, Sanghamitra Mohanty, Janet K. Han, Andrea Natale, Dhiraj Gupta, Andrea M. Russo, Amin Al‐Ahmad, Anne M. Gillis, Kevin L. Thomas

Bibliographic record

VenueCJC Open · 2021
Typereview
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsUniversity of Calgary
FundersBiosense WebsterBiotronikBoston Scientific CorporationPfizerMedtronicAbbott Laboratories
KeywordsEthnic groupMedicineAtrial fibrillationRace (biology)Political scienceInternal medicineSociologyGender studies

Abstract

fetched live from OpenAlex

Atrial fibrillation (AF) is the most common clinical arrhythmia, and it results in adverse outcomes and increased healthcare costs. Racial and ethnic differences in AF management, although recognized, are poorly understood. This review summarizes racial differences in AF epidemiology, genetics, clinical presentation, and management. In addition, it highlights the underrepresentation of racial and ethnic populations in AF clinical trials, especially trials focused on stroke prevention. Specific strategies are proposed for future research and initiatives that have potential to eliminate racial and ethnic differences in the care of patients with AF. Addressing racial and ethnic disparities in healthcare access, enrollment in clinical trials, resource allocation, prevention, and management will likely narrow the gaps in the care and outcomes of racial and ethnic minorities suffering from AF.

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)0.001
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.261
GPT teacher head0.461
Teacher spread0.199 · 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
GenreReview

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

Citations49
Published2021
Admission routes1
Has abstractyes

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