Prolonged cardiac monitoring after cryptogenic stroke superior to 24 h ECG in detection of occult paroxysmal atrial fibrillation
Bibliographic record
Abstract
Commentary on : Gladstone DJ, Spring M, Dorian P, et al. Atrial fibrillation in patients with cryptogenic stroke. N Eng J Med 2014;370:2467–77.[OpenUrl][1][CrossRef][2][PubMed][3][Web of Science][4] Stroke remains a prevalent and devastating condition for many people across the world, it is a leading cause of disability and is associated with significant monetary and social losses, yet is considered to be a largely preventable disease. One-third of all strokes are considered cryptogenic after initial diagnostic evaluations. Cryptogenic stroke has been identified, only recently, as an important area of additional investigation. Part of the EMBRACE trial, Gladstone and colleagues’ study adds to mounting evidence that prolonged cardiac monitoring is needed to identify paroxysmal atrial fibrillation (PAF) in patients with cryptogenic stroke. In this open-label, multicenter trial in Canada, 572 patients over the age of 55 with a recent cryptogenic stroke or transient ischaemic attack (TIA) were randomised to conventional 24 h ECG monitoring (Holter monitor) versus 30-day non-invasive, ambulatory ECG … [1]: {openurl}?query=rft.jtitle%253DN%2BEng%2BJ%2BMed%26rft.volume%253D370%26rft.spage%253D2467%26rft_id%253Dinfo%253Adoi%252F10.1056%252FNEJMoa1311376%26rft_id%253Dinfo%253Apmid%252F24963566%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/external-ref?access_num=10.1056/NEJMoa1311376&link_type=DOI [3]: /lookup/external-ref?access_num=24963566&link_type=MED&atom=%2Febmed%2F19%2F6%2F235.atom [4]: /lookup/external-ref?access_num=000337804400004&link_type=ISI
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.006 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.022 | 0.019 |
| Insufficient payload (model declined to judge) | 0.019 | 0.012 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".