1357Identifying the undiagnosed AF patient through “Know Your Pulse” community pharmacy based events held in ten countries during Arrhythmia Alliance World Heart Rhythm Week 2017
Bibliographic record
Abstract
Introduction: Atrial fibrillation (AF) is the most common arrhythmia, it is frequently asymptomatic with a third of people with AF currently undiagnosed. A manual pulse rhythm check can help improve detection rates and use of mobile ECG technology can help in its diagnosis. Previous studies have shown the value of Community Pharmacy in helping to opportunistically screen for people with various conditions including AF. Arrhythmia Alliance (A-A) hosts the annual Arrhythmia Alliance World Heart Rhythm Week and the campaign theme was “identifying the undiagnosed person with arrhythmia”. A-A partnered with The International Pharmacist for Anticoagulation Care Taskforce (iPACT) to host pulse awareness events (“Know Your Pulse” events – a proprietary A-A campaign) across ten countries during Arhythmia Alliance World Heart Rhythm Week. Aims: – Raise public awareness of pulse rhythm and connection to AF – Demonstrate effectiveness of opportunistic population screening for AF in Community Pharmacy setting, using a manual pulse check and single lead mobile ECG technology
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".