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Record W2547392174 · doi:10.1017/cjn.2016.297

Antithrombotic Strategy in Cerebral Venous Thrombosis: Differences Between Neurologist and Hematologist Respondents in a Canadian Survey

2016· article· en· W2547392174 on OpenAlexafffundvenueabout
Thalia S. Field, Marie‐Christine Camden, Sohaila Alshimemeri, Gary Lui, Agnes Lee

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2016
Typearticle
Languageen
FieldMedicine
TopicCerebral Venous Sinus Thrombosis
Canadian institutionsCentre hospitalier universitaire de QuébecUniversity of British Columbia
FundersCanadian Stroke ConsortiumVancouver Coastal Health Research InstituteChina Scholarship Council
KeywordsMedicineAntithromboticHeparinThrombosisVenous thrombosisNeurologyAnesthesiaIntensive care medicineEmergency medicineInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

Patterns of practice for management of cerebral venous thrombosis in Canada are unknown. We surveyed Canadian neurologists and hematologists regarding anticoagulation in cerebral venous thrombosis. The response rate was 28%, with 27 neurologists and 20 hematologists responding. We found that choice of first-line initial anticoagulation differed significantly between neurologists and hematologists, with 89% of neurologists favouring unfractionated heparin and hematologists' preference split between unfractionated heparin (50%) and low-molecular-weight heparin (50%). Differences in patterns of practice likely reflect clinical equipoise.

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)0.007
metaresearch head score (Gemma)0.005
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.621
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0010.009
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0000.001
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 head0.080
GPT teacher head0.312
Teacher spread0.233 · 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 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

Citations8
Published2016
Admission routes4
Has abstractyes

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