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Record W2560705311 · doi:10.1182/blood.v126.23.428.428

Outcomes of Low-Molecular-Weight Heparintreatment for Venous Thromboembolism in Patients with Primary and Metastatic Brain Tumors

2015· article· en· W2560705311 on OpenAlexaffabout
Chatree Chai‐Adisaksopha, Matthew Cheah, Alfonso Iorio, Mark Crowther, Lori Ann Linkins

Bibliographic record

VenueBlood · 2015
Typearticle
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsSt. Joseph’s Healthcare HamiltonSt. Joseph's HospitalMcMaster University
Fundersnot available
KeywordsMedicineRetrospective cohort studyCancerInternal medicineThrombosisIncidence (geometry)CohortVenous thrombosisBrain metastasisConfidence intervalLow molecular weight heparinDeep veinSurgeryMetastasis

Abstract

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Abstract Background: Venous thromboembolism (VTE) is one of the most common complications of patients with brain tumors. There is limited data available in the literature on VTE treatment in these patients compared to other cancer types. We evaluated the efficacy and safety of low-molecular weight heparin treatment for newly diagnosed VTE in patients with primary and metastatic brain tumours at a tertiary care centre. Methods: We conducted a matched retrospective cohort study of patients with primary or metastatic brain cancer who were diagnosed with cancer-associated VTE. Cases were selected after completion of 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, Ontario, Canada. Controls were age- and gender-matched patients with cancer-associated VTE from the same cohort, but without brain tumours. The primary outcome was first recurrent VTE and secondary outcomes were major bleeding and clinically relevant bleeding. Results: A total of 364 patients with cancer-associated thrombosis were included (182 with primary or metastatic brain tumors and 182 controls). The median follow-up duration was 6.7 (inter quartile range 2.5-15.8) months. The incidence rate of recurrent VTE was 11.0 per 100 patient-year (95% confidence interval [CI]; 6.7-17.9) in patients with brain tumors and 13.5 per 100 patient-year (95% CI; 9.3-19.7) in controls, incidence rate ratio [IRR]; 0.8 (95% CI; 0.4-1.5, p-value=0.43). There was no significant difference in the rate of recurrent VTE in the two groups (log-rank p-value=0.26, Figure 1). The incidence of major bleeding was 8.9 per 100 (95% CI; 5.2-15.4) patient-year in patients with brain tumors versus 6.0 per 100 patient-year (95% CI; 3.4-10.9) in controls, IRR; 0.8 (95% CI; 0.4-1.5, p-value=0.51). There were no significant differences in the risk of major bleeding (Figure 2) and clinical relevant bleeding between the two groups, log-rank p-value 0.9 and 0.8, respectively. When compared to controls, the rate of major gastrointestinal bleeding was lower in patients with brain tumours (0.6% versus 6.0%, p-value=0.003) whereas the rate of intracranial bleeding was higher (4.4% versus 0%, p-value=0.004). Subgroup analysis revealed that the incidence of intracranial bleeding in patients with primary brain tumors was higher than those with metastatic brain tumors, but did not reach statistical significant (6.0% vs 3.5%, p=0.008). Conclusions: Recurrent VTE, major bleeding and clinical relevant bleeding were not significantly different in patients with cancer-associated VTE in the setting of primary or metastatic brain tumours compared with controls. However, intracranial bleedings occurred more frequently in patients with brain tumours. Disclosures Linkins: Pfizer: Honoraria; Bayer: Honoraria, Research Funding.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.010
GPT teacher head0.241
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2015
Admission routes2
Has abstractyes

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