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Record W2165070231 · doi:10.1136/fg.2009.000257

Circumstances in which colonoscopy misses cancer: Table 1

2010· review· en· W2165070231 on OpenAlexaff
Linda Rabeneck, Lawrence Paszat

Bibliographic record

VenueFrontline Gastroenterology · 2010
Typereview
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsColonoscopyMedicinePolypectomyColorectal cancerGeneral surgeryIntubationAdenomaCancerBowel preparationSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Colonoscopy is associated with a varying risk of missing colorectal cancer (CRC). The objective of this paper was to review the existing evidence that indicates when colonoscopy may miss cancer in usual clinical practice and to provide information that would be helpful to endoscopists in their daily practice. CRC is diagnosed within 3 years in about 5% of persons with CRC who undergo colonoscopy in whom the cancer is not detected. Future research should be directed at disentangling the relative contributions of tumour biology and colonoscopy quality in explaining this result. When consent is obtained for colonoscopy, patients must be informed of the small risk that a cancer may not be detected. Steps that can be taken to address colonoscopy quality include formal training in colonoscopy and polypectomy technique, coupled with maintenance of skills by performing at least 300 colonoscopies per year. The use of split dose bowel preparation is advised. Colonoscopy should be completed to the caecum with documentation of landmarks (ileocaecal valve; appendiceal orifice). Careful colonoscopy technique includes examining the proximal sides of flexures and folds, washing and suctioning debris and ensuring adequate colonic distension. Caecal intubation and adenoma detection rates should be reported and reviewed. Lesions should be completely removed at polypectomy and attention given to appropriate surveillance.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.980
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.030
GPT teacher head0.345
Teacher spread0.315 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations43
Published2010
Admission routes1
Has abstractyes

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