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Record W3042794290 · doi:10.1503/cmaj.200021

Diagnosing acute aortic syndrome: a Canadian clinical practice guideline

2020· article· en· W3042794290 on OpenAlexaffvenueabout
Robert Ohle, Justin Yan, Krishan Yadav, Alexis Cournoyer, David W. Savage, Prasad Jetty, Rony Atoui, Bindu Bittira, Brock Wilson, Ashish Gupta, Niamh Coffey, Yvonne Callaway, Jeffrey Middaugh, Dominique R. Ansell, Fraser D. Rubens, Stephen J Bignucolo, Terena-Marie Scott, Sarah McIsaac, Eddy Lang

Bibliographic record

VenueCanadian Medical Association Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicAortic Disease and Treatment Approaches
Canadian institutionsUniversity of OttawaWestern University
Fundersnot available
KeywordsAcute aortic syndromeAortic dissectionMedicineAcute coronary syndromeGuidelineEmergency departmentChest painClinical PracticeMedical diagnosisCardiologyIntensive care medicineInternal medicineEmergency medicineRadiologyAortaMyocardial infarctionPhysical therapyPathology

Abstract

fetched live from OpenAlex

KEY POINTS Acute aortic syndrome (AAS) is a life-threatening emergency, accounting for 1/2000 presentations of acute chest or back pain to the emergency department.[1][1] It is a clinical spectrum of diagnoses including aortic dissection, intramural hematoma and penetrating atherosclerotic ulcer at

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)0.005
metaresearch head score (Gemma)0.024
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: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.973
Threshold uncertainty score0.708

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.024
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0070.007
Science and technology studies0.0030.001
Scholarly communication0.0030.002
Open science0.0040.003
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0070.004

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 head0.028
GPT teacher head0.336
Teacher spread0.308 · 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
GenreMethods

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

Citations48
Published2020
Admission routes3
Has abstractyes

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