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] Design: analysis of data from 6 randomised controlled trials (RCTs) to derive and validate a clinical prediction rule. ### ![Graphic][6] Setting: US, Canada, Denmark, and the Netherlands. ### ![Graphic][7] 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 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 | 0.005 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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, 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".