MétaCan
Menu
Back to cohort
Record W2135462148 · doi:10.3747/co.22.2392

Clinical Challenges in Patients with Cancer-Associated Thrombosis: Canadian Expert Consensus Recommendations

2015· article· en· W2135462148 on OpenAlexafffundvenueabout
Marc Carrier, Sudeep Shivakumar, Vicky Tagalakis, Peter L. Gross, Normand Blais, Charles Butts, Mark Crowther

Bibliographic record

VenueCurrent Oncology · 2015
Typearticle
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsSt. Joseph’s Healthcare HamiltonUniversity of AlbertaDalhousie UniversityWestern UniversityCentre Hospitalier de l’Université de MontréalThrombosis and Atherosclerosis Research InstituteOttawa Hospital
FundersLEO PharmaMcMaster UniversityHeart and Stroke Foundation of CanadaPfizer CanadaSanofiBristol-Myers Squibb
KeywordsMedicineMEDLINEIntensive care medicineDelphi methodExpert opinionEvidence-based medicineVenous thromboembolismVenous thrombosisAlternative medicineThrombosisFamily medicineSurgeryPathology

Abstract

fetched live from OpenAlex

Venous thromboembolism is a common complication in cancer patients, and thromboembolism is the second most common cause of death after cancer progression. A number of clinical practice guidelines provide recommendations for the management of cancer-associated thrombosis. However, the guidelines lack recommendations covering commonly encountered clinical challenges (for example, thrombocytopenia, recurrent venous thromboembolism, etc.) for which little or no evidence exists. Accordingly, recommendations were developed to provide expert guidance to medical oncologists and other health care professionals caring for patients with cancer-associated thrombosis. The current expert consensus was developed by a team of 21 clinical experts. For each identified clinical challenge, the literature in medline, embase, and Evidence Based Medicine Reviews was systematically reviewed. The quality of the evidence was assessed, summarized, and graded. Consensus statements were generated, and the experts voted anonymously using a modified Delphi process on their level of agreement with the various statements. Statements were progressively revised through separate voting iterations and were then finalized. Clinicians using these recommendations and suggestions should tailor patient management according to the risks and benefits of the treatment options, patient values and preferences, and local cost and resource allocations.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.281
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

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

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 head0.350
GPT teacher head0.473
Teacher spread0.123 · 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.

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

Citations46
Published2015
Admission routes4
Has abstractyes

Explore more

Same venueCurrent OncologySame topicVenous Thromboembolism Diagnosis and ManagementFrench-language works237,207