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Record W2397692051

Monitoring for Atrial Fibrillation in Discharged Stroke and Transient Ischemic Attack Patients: Recommendations

2016· article· en· W2397692051 on OpenAlexaff
Gino De Angelis, Karen Cimon, Alison Sinclair, Kelly Farrah, John A. Cairns, Adrián Baranchuk, Lauren E. Cipriano, Laura Weeks, Tamara Rader, Sarah Garland

Bibliographic record

VenueEurope PMC (PubMed Central) · 2016
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsWestern UniversityKingston General HospitalUniversity of British ColumbiaCanadian Agency for Drugs and Technologies in Health
Fundersnot available
KeywordsMedicineAtrial fibrillationStroke (engine)Cardiac monitoringCardiologyHolter monitorInternal medicineAmbulatoryImplantable loop recorderEmbolic strokeOccultElectrocardiographyIschemiaIschemic strokePathology
DOInot available

Abstract

fetched live from OpenAlex

An ischemic stroke is caused by thrombosis of the cerebral vessels or by emboli from a proximal arterial source or the heart. This blockage deprives the brain cells of vital oxygen and nutrients leading to cell death. A transient ischemic attack (TIA) is a neurological deficit lasting less than 24 hours, caused by cerebral ischemia.Atrial fibrillation (AF) is a type of cardiac arrhythmia, which causes pooling of blood that leads to thrombosis formation and may cause a stroke or TIA. Patients with AF but no history of stroke have a stroke risk of 4.5% per year; however, anticoagulation therapy, can reduce this risk to 1.4% per year. Often patients with AF will not have any symptoms, and therefore they are difficult to identify. Roughly 30% to 40% of first-time ischemic strokes are due to an unknown cause, and are referred to as an embolic stroke of undetermined source (ESUS). Patients who have experienced ESUS may have undiagnosed, or occult, AF. Determining whether they do have AF can be important to help prevent future strokes or TIAs.Long-term electrocardiography (ECG) monitoring using outpatient cardiac monitoring devices can identify occult AF that is undetectable by other means. To this end, outpatient cardiac monitoring devices providing increased mobility for patients and the ability to transmit data wirelessly have been developed, and allow for longer-term surveillance outside the hospital setting. These devices include ambulatory Holter monitors, external loop recorders (ELRs), mobile cardiac outpatient telemetry (MCOT) devices, and implantable loop recorders (ILRs).CADTH conducted a health technology assessment (HTA) on the clinical effectiveness and cost-effectiveness of cardiac monitoring devices in patients discharged from hospital following a stroke or TIA, to help inform decisions about these devices. Patient perspectives and experiences regarding the value and impact of outpatient AF cardiac monitoring devices were also considered.

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)0.002
metaresearch head score (Gemma)0.015
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: Methods · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0060.005
Insufficient payload (model declined to judge)0.0160.007

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 head0.053
GPT teacher head0.298
Teacher spread0.245 · 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
GenreMethods

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

Citations3
Published2016
Admission routes1
Has abstractyes

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