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Record W4210986385 · doi:10.3390/curroncol29020078

Report from the Western Canadian Gastrointestinal Consensus Cancer Conference—Management of Total Neoadjuvant Therapy in Rectal Cancer

2022· article· en· W4210986385 on OpenAlexaffvenueabout
Howard J. Lim, Aswin Abraham, Shahid Ahmed, Shahida Ahmed, Carl J. Brown, Bryan Brunet, Janine M. Davies, Corinne Doll, Dorie-Anna Dueck, Vallerie Gordon, Kimberly R. Hagel, Pamela Hebbard, Christina Kim, Duc Le, Richard M. Lee‐Ying, John Paul McGhie, Karen Mulder, Jason Y. Park, Daniel J. Renouf, Devin Schellenberg, Ralph Wong, Adnan Zaidi

Bibliographic record

VenueCurrent Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Surgical Treatments
Canadian institutionsSaskatchewan Cancer AgencyUniversity of British ColumbiaAlberta Health ServicesCancerCare ManitobaBC Cancer Agency
Fundersnot available
KeywordsMedicineColorectal cancerMultidisciplinary approachRadiation therapyFamily medicineSession (web analytics)Neoadjuvant therapyHealth careGastrointestinal cancerCancerInternal medicineBreast cancer

Abstract

fetched live from OpenAlex

An educational session related to the Western Canadian Gastrointestinal Cancer Consensus Conference (WCGCCC) was held virtually on 14 October 2020. The WCGCCC is an interactive multidisciplinary conference 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 cancer. Surgical, medical, and radiation oncologists; pathologists, radiologists, and allied health care professionals 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 total neoadjuvant therapy in rectal 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)0.016
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: Other · Consensus signal: none
Teacher disagreement score0.470
Threshold uncertainty score0.945

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.024
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 science0.0020.001
Research integrity0.0060.006
Insufficient payload (model declined to judge)0.0050.002

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 head0.157
GPT teacher head0.417
Teacher spread0.260 · 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

Citations0
Published2022
Admission routes3
Has abstractyes

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