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Record W3199772168 · doi:10.3390/curroncol28050310

Report from the 21st Annual Western Canadian Gastrointestinal Cancer Consensus Conference; Calgary, Alberta; 20–21 September 2019

2021· article· en· W3199772168 on OpenAlexaffvenueabout
Richard M. Lee‐Ying, Osama Ahmed, Shahid Ahmed, Shahida Ahmed, Oliver F. Bathe, Bryan Brunet, Laura A. Dawson, Janine M. Davies, Valerie Gordon, Pamela Hebbard, Jessica Kasnik, Christina Kim, Duc Le, Michael K.C. Lee, Howard J. Lim, John Paul McGhie, Karen Mulder, Jason Y. Park, Daniel J. Renouf, Vincent C. Tam, Robin Visser, Ralph Wong, Adnan Zaidi, Corinne Doll

Bibliographic record

VenueCurrent Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicPancreatic and Hepatic Oncology Research
Canadian institutionsUniversity of ManitobaBC Cancer AgencyPrincess Margaret Cancer CentreCancerCare ManitobaSaskatchewan Cancer AgencyUniversity Health NetworkAlberta Health Services
Fundersnot available
KeywordsMedicineFamily medicineGastrointestinal cancerHealth professionalsPresentation (obstetrics)Health careConsensus conferenceCancerSurgeryPolitical scienceInternal medicineColorectal cancer

Abstract

fetched live from OpenAlex

The 21st annual Western Canadian Gastrointestinal Cancer Consensus Conference (WCGCCC) was held in Calgary, Alberta, 20-21 September 2019. The WCGCCC is an interactive multi-disciplinary conference attended by health care professionals from across Western Canada (British Columbia, Alberta, Saskatchewan, and Manitoba) involved in the care of patients with gastrointestinal cancer. Surgical, medical, and radiation oncologists, pathologists, radiologists, and allied health care professionals such as dietitians and nurses participated in presentation and discussion sessions to develop the recommendations presented here. This consensus statement addresses current issues in the management of hepato-pancreato-biliary (HPB) cancers.

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)0.011
metaresearch head score (Gemma)0.012
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: Editorial · Consensus signal: none
Teacher disagreement score0.455
Threshold uncertainty score0.916

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0030.001
Scholarly communication0.0030.001
Open science0.0020.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0370.010

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 head0.107
GPT teacher head0.421
Teacher spread0.314 · 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
GenreEditorial

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

Citations2
Published2021
Admission routes3
Has abstractyes

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