MétaCan
Menu
Back to cohort
Record W4386283273 · doi:10.3390/curroncol30090579

Report from the 24th Annual Western Canadian Gastrointestinal Cancer Consensus Conference on Colorectal Cancer, Richmond, British Columbia, 28–29, October 2022

2023· article· en· W4386283273 on OpenAlexaffvenueabout
Sharlene Gill, Shahid Ahmed, Brady Anderson, Scott Berry, Howard J. Lim, P. Terry Phang, Ankur Sharma, João Paulo Solar Vasconcelos, Karamjit Gill, Mussawar Iqbal, Keith Tankel, Theresa W. Chan, Magdalena Recsky, Jennifer Nuk, James T. Paul, Shazia Mahmood, Karen Mulder

Bibliographic record

VenueCurrent Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Surgical Treatments
Canadian institutionsUniversity of CalgaryUniversity of British ColumbiaQueen's UniversitySaskatchewan Cancer AgencyCancerCare ManitobaUniversity of ManitobaKelowna General HospitalBC Cancer Agency
FundersAstellas PharmaIncyteTaiho PharmaceuticalAstraZenecaAmgenPfizerBristol-Myers SquibbEisaiBeiGeneMerck
KeywordsMedicineColorectal cancerCancerGastrointestinal cancerConsensus conferenceGeneral surgeryFamily medicineInternal medicine

Abstract

fetched live from OpenAlex

The 24th annual Western Canadian Gastrointestinal Cancer Consensus Conference (WCGCCC) was held in Richmond, British Columbia, on 28-29 October 2022. The WCGCCC is an interactive multidisciplinary conference attended by healthcare professionals from across Western Canada (British Columbia, Alberta, Saskatchewan, and Manitoba) who are involved in the care of patients with gastrointestinal cancer. Surgical, medical, and radiation oncologists; pathologists; radiologists; and allied health care professionals such as dieticians, nurses and a genetic counsellor participated in presentation and discussion sessions for the purpose of developing the recommendations presented here. This consensus statement addresses current issues in the management of colorectal cancer.

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)0.022
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 score0.533
Threshold uncertainty score0.929

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0030.001
Scholarly communication0.0040.001
Open science0.0020.002
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0230.006

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 head0.087
GPT teacher head0.383
Teacher spread0.295 · 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

Citations2
Published2023
Admission routes3
Has abstractyes

Explore more

Same venueCurrent OncologySame topicColorectal Cancer Surgical TreatmentsFrench-language works237,207