A prediction rule identified patients with atrial fibrillation at low risk of stroke while taking aspirin
Bibliographic record
Abstract
van Walraven C, Hart RG, Wells GA, et al . A clinical prediction rule to identify patients with atrial fibrillation and a low risk for stroke while taking aspirin. Arch Intern Med 2003;163:936–43. [OpenUrl][1][CrossRef][2][PubMed][3][Web of Science][4] Q What is the accuracy of an age independent clinical prediction rule for identifying patients with non-valvular atrial fibrillation (AF) who are at low risk of all cause stroke or transient ischaemic attack (TIA) while taking aspirin? Clinical impact ratings GP/FP/Primary care ★★★★★★☆ IM/Ambulatory ★★★★★★☆ Geriatrics ★★★★★★☆ Cardiology ★★★★★★☆ ### ![Graphic][5]</img>Design: analysis of data from 6 randomised controlled trials (RCTs) to derive and validate a clinical prediction rule. ### ![Graphic][6]</img>Setting: US, Canada, Denmark, and the Netherlands. ### ![Graphic][7]</img>Patients: 2501 patients (mean age 70 y, 67% men, 93% white) with non-valvular AF who were participating in 1 of 6 RCTs. All patients had had no stroke or TIA for at least 6–24 months before entering in the trials and received aspirin at dosages between 75–325 mg/day. In most studies, patients were excluded if they had clinical indications for or contraindications to oral anticoagulation or aspirin therapy, or if they had a recent acute coronary syndrome or cardiac revascularisation. Patients were randomly … [1]: {openurl}?query=rft.jtitle%253DArchives%2Bof%2BInternal%2BMedicine%26rft.stitle%253DArch%2BIntern%2BMed%26rft.issn%253D0003-9926%26rft.aulast%253Dvan%2BWalraven%26rft.auinit1%253DC.%26rft.volume%253D163%26rft.issue%253D8%26rft.spage%253D936%26rft.epage%253D943%26rft.atitle%253DA%2BClinical%2BPrediction%2BRule%2Bto%2BIdentify%2BPatients%2BWith%2BAtrial%2BFibrillation%2Band%2Ba%2BLow%2BRisk%2Bfor%2BStroke%2BWhile%2BTaking%2BAspirin%26rft_id%253Dinfo%253Adoi%252F10.1001%252Farchinte.163.8.936%26rft_id%253Dinfo%253Apmid%252F12719203%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=10.1001/archinte.163.8.936&link_type=DOI [3]: /lookup/external-ref?access_num=12719203&link_type=MED&atom=%2Febmed%2F9%2F1%2F29.atom [4]: /lookup/external-ref?access_num=000182477400010&link_type=ISI [5]: /embed/inline-graphic-1.gif [6]: /embed/inline-graphic-2.gif [7]: /embed/inline-graphic-3.gif
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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 | Observational | low |
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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.
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".