MétaCan
Menu
Back to cohort
Record W3113720231 · doi:10.3747/co.27.6919

Canadian Guidelines on the Management of Colorectal Peritoneal Metastases

2020· article· en· W3113720231 on OpenAlexaffvenueabout
Alexandre Brind’Amour, Pierre‐André Dubé, JEAN-FRANÇOIS TREMBLAY, Mikaël Soucisse, Lloyd A. Mack, Antoine Bouchard‐Fortier, J. Andrea McCart, Anand Govindarajan, Danielle A. Bischof, Erika Haase, Carman A. Giacomantonio, Pamela Hebbard, Rami Younan, Andrea J. MacNeill, Cindy Boulanger-Gobeil, Lucas Sidéris

Bibliographic record

VenueCurrent Oncology · 2020
Typearticle
Languageen
FieldMedicine
TopicIntraperitoneal and Appendiceal Malignancies
Canadian institutionsUniversity of British ColumbiaUniversity of ManitobaDalhousie UniversityUniversity of TorontoMount Sinai HospitalUniversity of AlbertaUniversité de MontréalUniversité LavalCentre Hospitalier de l’Université de MontréalUniversity of CalgaryHôpital Maisonneuve-Rosemont
Fundersnot available
KeywordsMedicineColorectal cancerPeritoneal carcinomatosisInternal medicineCancer researchGeneral surgeryOncologyCancer

Abstract

fetched live from OpenAlex

Modern management of colorectal cancer (crc) with peritoneal metastasis (pm) is based on a combination of cytoreductive surgery (crs), systemic chemotherapy, and hyperthermic intraperitoneal chemotherapy (hipec). Although the role of hipec has recently been questioned with respect to results from the prodige 7 trial, the role and benefit of a complete crs were confirmed, as observed with a 41-month gain in median survival in that study, and 15% of patients remaining disease-free at 5 years. Still, crc with pm is associated with a poor prognosis, and good patient selection is essential. Many questions about the optimal management approach for such patients remain, but all patients with pm from crc should be referred to, or discussed with, a pm surgical oncologist, because cure is possible. The objective of the present guideline is to offer a practical approach to the management of pm from crc and to reflect on the new practice standards set by recent publications on the topic.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.634
Threshold uncertainty score0.575

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.176
GPT teacher head0.400
Teacher spread0.224 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations11
Published2020
Admission routes3
Has abstractyes

Explore more

Same venueCurrent OncologySame topicIntraperitoneal and Appendiceal MalignanciesFrench-language works237,207