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Record W4210398918 · doi:10.1002/rth2.12603

Abstracts from the CanVECTOR 2021 Annual Conference November 5th, 2021

2021· article· en· W4210398918 on OpenAlexaffabout
Maha Othman, Cynthia Wu, Sudeep Shivakumar, Lisa Duffett, Deb Siegal, Alejandro Lazo- Langner, Zach Liederman, Rick Ikesaka, Jessie Micholuk, Arshia Alimohammadi, Zhou, Souza, Rebchuck, Field, Ann E. Clarke, Prism Schneider, Leslie Skeith, Robert Korley

Bibliographic record

VenueResearch and Practice in Thrombosis and Haemostasis · 2021
Typearticle
Languageen
FieldDecision Sciences
TopicAcademic Publishing and Open Access
Canadian institutionsUniversity of British ColumbiaUniversity of Calgary
Fundersnot available
KeywordsVirologyLibrary sciencePolitical scienceMedicineComputer science

Abstract

fetched live from OpenAlex

The treatment and course of cerebral venous thrombosis (CVT) associated with traumatic brain injury (TBI) is not well-characterized and is often managed conservatively. We reviewed the experience at a large tertiary Canadian hospital.

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)0.016
metaresearch head score (Gemma)0.073
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.526
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.073
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0050.004
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.379
GPT teacher head0.528
Teacher spread0.149 · 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
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
Published2021
Admission routes2
Has abstractyes

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