Seven days of non-invasive cardiac monitoring early postischaemic stroke or TIA increases atrial fibrillation detection rate compared with current guideline-based practice
Bibliographic record
Abstract
Commentary on: Higgins P, Macfarlane PW, Dawson J, et al. Non-invasive cardiac event monitoring to detect atrial fibrillation after ischemic stroke: a randomized, controlled trial. Stroke 2013;44:2525–31.[OpenUrl][1][Abstract/FREE Full Text][2] Atrial fibrillation (AF) is an established risk factor for stroke, and anticoagulation treatment is effective in reducing recurrent stroke risk. Guidelines recommend the use of clinical prediction tools to select patients with AF for anticoagulation therapy. It has long been recognised that paroxysmal AF (PAF) may pose a similar stroke risk to persistent AF, but the association between the duration of PAF and stroke risk remains uncertain. The best method to detect PAF has yet to be conclusively determined.1 ,2 This randomised controlled trial examines whether prolonged, non-invasive cardiac monitoring poststroke is superior to guideline-based standard treatment in PAF detection. The trial assessed the detection of AF in … [1]: {openurl}?query=rft.jtitle%253DStroke%26rft_id%253Dinfo%253Adoi%252F10.1161%252FSTROKEAHA.113.001927%26rft_id%253Dinfo%253Apmid%252F23899913%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/ijlink?linkType=ABST&journalCode=strokeaha&resid=44/9/2525&atom=%2Febmed%2F19%2F4%2F152.atom
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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: Commentary About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 0.000 |
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".