TIME TRENDS IN INTRACRANIAL BLEEDING AND NEW ORAL ANTICOAGULANT PRESCRIPTION
Bibliographic record
Abstract
Objectives & Background Dabigatran, rivaroxaban and apixaban were approved for stroke prevention in the past 4 years. Phase 3 studies reported a lower risk of intracranial bleeding compared to warfarin however there is little real-life data to validate this. We assessed time trends in oral anticoagulant (OAC) associated intracranial bleeding between 2009 and 2013. We compared bleeding rates to provincial OAC prescription trends. Methods ICD-10 codes were used to identify all atraumatic intracranial bleeds presenting to our neurosurgical centre (covering a population of 1.3 million). Trained researchers extracted data on anticoagulant medication in the week prior to diagnosis of intracranial bleed. Provincial prescription data for OACs were obtained from IMS Brogan CompuScript Market Dynamics. The primary outcome was the incident OAC-associated intracranial bleed time trend between 2009 and 2013. The secondary outcomes were the non-OAC associated intracranial bleed time trend, and the provincial OAC prescription trends. Results 2050 patients presented with atraumatic intracranial bleeds. 371 (18%) patients were prescribed an anticoagulant, of which 335 were OACs. There was an increasing trend over time in the rate of anticoagulant associated bleeding (p=0.009) and non-anticoagulant associated bleeding (p=0.063). Warfarin accounted for a disproportionately large number of all OAC-associated bleeds compared to prescription prevalence. Dabigatran, rivaroxaban and apixaban accounted for a smaller proportion of OAC bleeds when compared to prescription prevalence. Conclusion We found an increasing number of patients treated for intracranial bleeding over time. Warfarin accounted for a disproportionate number of intracranial bleeds and the new oral anticoagulants, fewer than expected.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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, 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".