P5804Who should be screened for paroxysmal atrial fibrillation?
Bibliographic record
Abstract
Background: Paroxysmal atrial fibrillation (PAF) is a common cause of thromboembolic stroke and is far more frequent than clinical symptoms would predict. The frequency of silent PAF depends on the method of investigation, the longer the period of ECG monitoring the more likely one is to detect an episode of PAF. Clearly if we could more accurately define an at-risk population the yield of prolonged monitoring may be improved? The purpose of our investigation was to attempt to define using Holter and ECHO data who may be at greater risk of PAF? Methods: Our database was searched for all patients who had a 24hr or 48hr Holter monitor and an echocardiogram within 180 days of each test. Patients were then divided into 4 groups, group 1 had no PACs during monitoring, group 2 had <100PACs/hr. and at least 1 atrial run, group 3 had >99 PACs/hr. and at least 1 atrial run, and group 4 had PAF. An atrial run was defined as >3 consecutive atrial beats at a rate >100bpm and <30 seconds in duration. Patients were compared with respect to age, atrial runs on Holter, LVEF, LA volume index and LV mass index. One-way ANOVA was used to assess overall differences between the mean values and Tukey-Kramer inter-comparison testing was used to determine differences between the various groups. The unpaired t-test was used to assess differences between the means. A p value of <0.05 was statistically significant.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".