The Outpatient Bleeding Risk Index predicted major bleeding in patients taking warfarin
Bibliographic record
Abstract
Aspinall SL, DeSanzo BE, Trilli LE, et al. Bleeding Risk Index in an anticoagulation clinic. Assessment by indication and implications for care. J Gen Intern Med 2005;20:1008–13. [OpenUrl][1][CrossRef][2][PubMed][3][Web of Science][4] Q In patients taking warfarin, how accurate is the Outpatient Bleeding Risk Index for predicting major bleeding? Clinical impact ratings Internal medicine ★★★★★★☆ Cardiology ★★★★★★☆ Haematology ★★★★★★☆ Neurology ★★★★★☆☆ ### ![Graphic][5] Design: retrospective cohort study to validate the previously developed Outpatient Bleeding Risk Index in a veteran population. ### ![Graphic][6] Setting: a pharmacist run anticoagulation clinic at the Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, Pennsylvania, USA. ### ![Graphic][7] Patients: 1269 patients (mean age 68 y, 92% men, 82% white) taking warfarin for <1 to >36 months. ### ![Graphic][8] Description of prediction guide: the Outpatient Bleeding Risk Index (range 0–4) is a summation of 4 factors: (i) age (⩾65 y = 1), (ii) history of stroke = 1, (iii) history of gastrointestinal bleeding = 1, and (iv) presence of ⩾1 serious comorbidity (recent myocardial … [1]: {openurl}?query=rft.jtitle%253DJournal%2Bof%2Bgeneral%2Binternal%2Bmedicine%2B%253A%2B%2Bofficial%2Bjournal%2Bof%2Bthe%2BSociety%2Bfor%2BResearch%2Band%2BEducation%2Bin%2BPrimary%2BCare%2BInternal%2BMedicine%26rft.stitle%253DJ%2BGen%2BIntern%2BMed%26rft.aulast%253DAspinall%26rft.auinit1%253DS.%2BL.%26rft.volume%253D20%26rft.issue%253D11%26rft.spage%253D1008%26rft.epage%253D1013%26rft.atitle%253DBleeding%2BRisk%2BIndex%2Bin%2Ban%2Banticoagulation%2Bclinic.%2BAssessment%2Bby%2Bindication%2Band%2Bimplications%2Bfor%2Bcare.%26rft_id%253Dinfo%253Adoi%252F10.1111%252Fj.1525-1497.2005.0229.x%26rft_id%253Dinfo%253Apmid%252F16307625%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=10.1111/j.1525-1497.2005.0229.x&link_type=DOI [3]: /lookup/external-ref?access_num=16307625&link_type=MED&atom=%2Febmed%2F11%2F4%2F120.atom [4]: /lookup/external-ref?access_num=000232892800006&link_type=ISI [5]: /embed/inline-graphic-1.gif [6]: /embed/inline-graphic-2.gif [7]: /embed/inline-graphic-3.gif [8]: /embed/inline-graphic-4.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: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Commentary 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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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.
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".