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Record W4225264047 · doi:10.1016/j.ctarc.2022.100557

How well do European patients understand cancer-associated thrombosis? A patient survey

2022· review· en· W4225264047 on OpenAlexaff
Anna Falanga, Charis Girvalaki, Manuel Monréal, Jacob C. Easaw, Annie Young

Bibliographic record

VenueCancer Treatment and Research Communications · 2022
Typereview
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsAlberta Cancer FoundationUniversity of Alberta
Fundersnot available
KeywordsMedicineCancerThrombosisPsychological interventionMEDLINEFamily medicineIntensive care medicineNursingInternal medicine

Abstract

fetched live from OpenAlex

Ongoing concerns regarding the morbidity and mortality from cancer-associated thrombosis led the European Cancer Patient Coalition (ECPC), the voice of cancer patients across Europe, to create a pan-European cancer-associated awareness patient survey to assess cancer-associated thrombosis (CAT) knowledge among a large population of patients with cancer. The ECPC survey represents the largest of its kind among patients/caregivers with CAT. It identified significant gaps in patient awareness and knowledge of CAT as well as a need for educational CAT-related discussions and interventions between healthcare professionals and patients with cancer and their caregivers. The aim of this paper is to highlight these gaps and to provide awareness of what/when information should be shared with patients/caregivers. Notably, the importance of providing information on how to reduce their risk of CAT, the role of anticoagulant prophylaxis and treatment (short- and long-term) including possible side-effects, and finally how to identify CAT symptoms early. Here we outline what type of information should be provided, as well as when and how to best discuss CAT with our oncology patients and their caregivers along the cancer care continuum, to reduce the risk of CAT and associated complications with a goal of improving patient outcomes.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.967
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.415
GPT teacher head0.478
Teacher spread0.064 · 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 teacher head, not a consensus.

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

Citations11
Published2022
Admission routes1
Has abstractyes

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