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Record W4297198660 · doi:10.1177/08465371221122807

Canadian Association of Radiologists/Canadian Association for Interventional Radiology/Canadian Society of Thoracic Radiology Guidelines on Thoracic Interventions

2022· review· en· W4297198660 on OpenAlexaffabout
Elsie T. Nguyen, Hamid Bayanati, Casey Hurrell, Matthew Aitken, Edward M. Cheung, Ashish Gupta, Scott R. Harris, T. Sedlic, Jana Taylor, G. Gahide, Carole Dennie

Bibliographic record

VenueCanadian Association of Radiologists Journal · 2022
Typereview
Languageen
FieldMedicine
TopicLung Cancer Diagnosis and Treatment
Canadian institutionsUniversity of OttawaMcGill University Health CentreUniversity of British ColumbiaVancouver General HospitalOttawa HospitalHealth Sciences CentreMemorial University of NewfoundlandUniversity of AlbertaSt. Michael's HospitalToronto General HospitalUniversité de SherbrookeUniversity of Toronto
Fundersnot available
KeywordsMedicinePsychological interventionThoracentesisRadiologyInterventional radiologyMediastinumCardiothoracic surgerySurgeryPleural effusionNursing

Abstract

fetched live from OpenAlex

Thoracic interventions are frequently performed by radiologists, but guidelines on appropriateness criteria and technical considerations to ensure patient safety regarding such interventions is lacking. These guidelines, developed by the Canadian Association of Radiologists, Canadian Association for Interventional Radiology and Canadian Society of Thoracic Radiology focus on the interventions commonly performed by thoracic radiologists. They provide evidence-based recommendations and expert consensus informed best practices for patient preparation; biopsies of the lung, mediastinum, pleura and chest wall; thoracentesis; pre-operative lung nodule localization; and potential complications and their management.

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)0.003
metaresearch head score (Gemma)0.013
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.149
Threshold uncertainty score0.296

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0100.010
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0030.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0200.006

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 head0.104
GPT teacher head0.423
Teacher spread0.318 · 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
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

Citations5
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Association of Radiologists JournalSame topicLung Cancer Diagnosis and TreatmentFrench-language works237,207