Outcomes of low-molecular-weight heparin treatment for venous thromboembolism in patients with primary and metastatic brain tumours
Bibliographic record
Abstract
Venous thromboembolism (VTE) is one of the most common complications in patients with brain tumours. There is limited data available in the literature on VTE treatment in these patients. We conducted a matched retrospective cohort study of patients with primary or metastatic brain cancer who were diagnosed with cancer-associated VTE. Patients were selected after a retrospective chart review of consecutive patients who were diagnosed with cancer-associated VTE between January 2010 and January 2014 at the Juravinski Thrombosis Clinic, Hamilton, Canada. Controls were age- and gender-matched patients with cancer-associated VTE from the same cohort, but without known brain tumours. A total of 364 patients with cancer-associated VTE were included (182 with primary or metastatic brain tumours and 182 controls). The median follow-up duration was 6.7 (interquartile range 2.5-15.8) months. The incidence rate of recurrent VTE was 11.0 per 100 patient-years (95 % CI; 6.7-17.9) in patients with brain tumours and 13.5 per 100 patient-years (95 % CI; 9.3-19.7) in non-brain tumour group. The incidence of major bleeding was 8.6 per 100 (95 % CI; 4.8-14.7) patient-years in patients with brain tumours versus 5.0 per 100 patient-years (95 % CI; 2.8-9.2) in controls. Rate of intracranial bleeding was higher in brain tumour patients (4.4 % vs 0 %, p-value=0.004). In summary, rates of recurrent VTE and major bleeding were not significantly different in patients with cancer-associated VTE in the setting of primary or metastatic brain tumours compared those without known brain tumours. However, greater numbers of intracranial bleeds were observed in patients with brain tumours.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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, 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".