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Record W2997388501 · doi:10.3747/co.26.5517

Report from the 20th Annual Western Canadian Gastrointestinal Cancer Consensus Conference; Saskatoon, Saskatchewan; 28–29 September 2018

2019· article· en· W2997388501 on OpenAlexaffvenueabout
Duc Le, Shahid Ahmed, Bryan Brunet, Janine M. Davies, C. Döll, M. Ferguson, Nathan Ginther, Vinessa Gordon, Trevor D. Hamilton, Pamela Hebbard, Ramzi M. Helewa, Christina Kim, Richard M. Lee‐Ying, Howard J. Lim, Jonathan M. Loree, John Paul McGhie, Karen Mulder, Jungeun Park, Daniel J. Renouf, Ralph Wong, A. Zaidi, Tooba Asif

Bibliographic record

VenueCurrent Oncology · 2019
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Surgical Treatments
Canadian institutionsUniversity of ManitobaUniversity of British ColumbiaAlberta Health ServicesSaskatchewan Cancer AgencyCancerCare ManitobaUniversity of Saskatchewan
Fundersnot available
KeywordsMedicineMultidisciplinary approachHealth professionalsFamily medicineHealth carePolitical science

Abstract

fetched live from OpenAlex

The 20th annual Western Canadian Gastrointestinal Cancer Consensus Conference was held in Saskatoon, Saskatchewan, 28-29 September 2018. This interactive multidisciplinary conference is attended by health care professionals from across Western Canada (British Columbia, Alberta, Saskatchewan, and Manitoba) who are involved in the care of patients with gastrointestinal cancers. In addition, invited speakers from other provinces participate. Surgical, medical, and radiation oncologists, and allied health care professionals participated in presentations and discussion sessions for the purpose of developing the recommendations presented here. This consensus statement addresses current issues in the management of colorectal 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)0.015
metaresearch head score (Gemma)0.021
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: Other · Consensus signal: none
Teacher disagreement score0.979
Threshold uncertainty score0.866

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.021
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0030.001
Scholarly communication0.0030.001
Open science0.0020.002
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0460.010

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 head0.077
GPT teacher head0.376
Teacher spread0.299 · 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
GenreOther

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

Citations1
Published2019
Admission routes3
Has abstractyes

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