The Outpatient Bleeding Risk Index
Bibliographic record
Abstract
BACKGROUND: Long-term anticoagulation prevents recurrent thrombosis in patients with idiopathic deep venous thrombosis or pulmonary embolism, but with a risk of clinically important so-called major bleeding. Physician- and patient-based decisions on the optimal duration of therapy are sensitive to the bleeding risk. The Outpatient Bleeding Risk Index potentially provides a means of calculating the potential risk of bleeding using easily elicited clinical findings, but, to our knowledge, the authors of the index have provided the only published validation of it. We sought to determine the accuracy of the index in our population of patients. METHODS: We prospectively applied the Outpatient Bleeding Risk Index to consecutive patients in our clinic who had been objectively diagnosed as having pulmonary embolism or deep venous thrombosis and who were about to undergo standard therapy. Standard therapy consisted of a minimum of 5 days of low-molecular-weight heparin therapy overlapped with warfarin sodium therapy, and continuation of warfarin therapy for at least 3 months, with a target international normalized ratio of 2.5. Patients were placed in 3 risk groups (low, moderate, or high), as defined by the index. The survival curves of the groups, using major hemorrhages as the events, were then compared by the log-rank test. RESULTS: Bleeding rates were lower than expected, but the index did discriminate between low- and moderate-risk groups (P =.03, log-rank test). The rate of major hemorrhage per 100 person-years was 0% (95% confidence interval, 0%-2.8%) in the low-risk group and 4.3% (95% confidence interval, 1.1%-11.1%) in the moderate-risk group. The rate in the high-risk group could not be defined because only 2 patients were at high risk. CONCLUSION: The Outpatient Bleeding Risk Index discriminates between low- and moderate-risk patients, and could be used to guide decisions on the optimal duration of anticoagulant therapy.
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How this classification was reachedexpand
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".