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Record W4213141299 · doi:10.1111/bjh.18085

Cardiovascular disease in hereditary haemophilia: The challenges of longevity

2022· review· en· W4213141299 on OpenAlexfundno aff
Susan Shapiro, Gary Benson, Gillian Evans, Catherine Harrison, Sarah Mangles

Bibliographic record

VenueBritish Journal of Haematology · 2022
Typereview
Languageen
FieldMedicine
TopicHemophilia Treatment and Research
Canadian institutionsnot available
FundersMedical Research Council CanadaMedical Research CouncilDepartment of Health and Aged Care, Australian GovernmentNational Institute for Health and Care ResearchNIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer Research
KeywordsMedicineHaemophiliaLife expectancyAntithromboticDiseaseCoronary artery diseaseAtrial fibrillationStroke (engine)Secondary preventionPopulationLongevityIntensive care medicinePediatricsInternal medicineGerontology

Abstract

fetched live from OpenAlex

The development of effective and safe treatments has significantly increased the life expectancy of persons with haemophilia (PWH). This has been accompanied by an increase in the comorbidities of ageing including cardiovascular disease, which poses particular challenges due to the opposing risks of bleeding from haemophilia and antithrombotic treatments versus thrombosis. Although mortality secondary to coronary artery disease in PWH is less than in the general population, the rate of atherosclerosis appears similar. The prevalence of atrial fibrillation in PWH and risk of secondary thromboembolic stroke are not well established. PWH can be safely supported through acute coronary interventions but data on the safety and efficacy of long-term antithrombotics are scarce. Increased awareness and research on cardiovascular disease in PWH will be crucial to improve primary prevention, acute management, secondary prevention and to best support ageing PWH.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.091
GPT teacher head0.343
Teacher spread0.251 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations27
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueBritish Journal of HaematologySame topicHemophilia Treatment and ResearchFrench-language works237,207