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Record W2444590564 · doi:10.1503/cjs.005116

Preoperative repeat endoscopy for colorectal cancer: What is its role and when is it necessary?

2016· article· en· W2444590564 on OpenAlexaffvenue
Woo Jin Choi, Michelle C. Cleghorn, Fayez A. Quereshy

Bibliographic record

VenueCanadian Journal of Surgery · 2016
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsMedicineEndoscopyColorectal cancerGeneral surgeryMEDLINEColonoscopyPatient careCancerMedical physicsSurgeryInternal medicineNursing

Abstract

fetched live from OpenAlex

SUMMARY: Many surgeons consider repeat endoscopy to be the standard of care for colorectal cancer; however, its utility in the preoperative setting is not well understood, especially given the lack of standardized guidelines on appropriate tumour localization and colonoscopic reporting. This often results in patients undergoing an unnecessary medical procedure during their preoperative evaluation. We discuss some of the issues surrounding the practice of preoperative repeat endoscopy as well as patient perspectives on the procedure. Our observations suggest that repeat endoscopy in the setting of colorectal cancer surgery may play a role in enabling transition of patient care between the initial endoscopist and the treating surgeon and in improving the patient experience. Patients with operable colorectal cancer appear to understand and support the current use of repeat endoscopy. However, improving preoperative care will require further research and ultimately the development of evidence-based clinical guidelines.

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)0.007
metaresearch head score (Gemma)0.046
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.046
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0010.003
Scholarly communication0.0030.004
Open science0.0020.001
Research integrity0.0030.005
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 head0.037
GPT teacher head0.279
Teacher spread0.242 · 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 designObservational
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

Citations4
Published2016
Admission routes2
Has abstractyes

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