P.058 Software algorithms for atrial fibrillation screening with wearable devices: a systematic review
Bibliographic record
Abstract
Background: Atrial fibrillation (AF) is an important risk factor for ischemic stroke but has no recognized screening method. Wearable devices have the potential to provide near continuous monitoring to detect AF. This systematic review evaluates the current state of software capable of detecting AF using wearable devices. Methods: We conducted a systematic search using PRISMA method of Medline, CENTRAL, PubMed and trial registries up to January 15, 2017. Abstracts and titles were screened, and relevant articles reviewed fully. English articles were selected if reporting on (1) software for AF detection (2) using heart rhythm signal, (3) theoretically applicable to wearable technology. Quality was evaluated with Cochrane GRADE. Results: Of 269 unique abstracts, 54 were identified for full review. 20 studies met inclusion criteria for algorithm accuracy analysis. Sensitivity and specificity ranged from 87.0 - 97.6% and 89.0 - 99.6%, respectively. 4 studies analyzed signal acquired using mobile devices with similar accuracy. Algorithms were potentially portable to wearable devices. Qualitative observations on the state and applicability of technology were made. Conclusions: Software analysing heart rhythm may be accurate for AF screening, but has not been tested on wearable devices. Such technology is promising but may be limited by hardware accuracy and high false positive rates.
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 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 | 0.008 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
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".