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Record W1889363944 · doi:10.1155/2008/901250

Incidental Colorectal Computed Tomography Abnormalities: Would You Send every Patient for a Colonoscopy?

2008· article· en· W1889363944 on OpenAlexvenueno aff
E Stermer, Alexandra Lavy, Tova Rainis, Omer Goldstein, Dean Keren, Abdel‐Rauf Zeina

Bibliographic record

VenueCanadian Journal of Gastroenterology · 2008
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsnot available
Fundersnot available
KeywordsColonoscopyComputed tomographyMedicineRadiologyColorectal cancerInternal medicineCancer

Abstract

fetched live from OpenAlex

BACKGROUND: The clinical significance of colorectal wall thickening (CRWT) in patients undergoing abdominal computed tomography (CT) has not yet been definitively established. OBJECTIVES: To compare alleged findings on abdominal CT with those of a follow-up colonoscopy. METHODS: Ninety-four consecutive patients found to have large-bowel abnormalities on abdominal CT were referred for colonoscopy. Of these patients, 48 were referred for a suspected colorectal tumour and 46 for CRWT. Colonoscopy was performed and findings were compared. RESULTS: Of the 48 suspected colorectal tumours, 34 were determined to be neoplastic lesions on colonoscopy. Of these, 26 were malignant and eight were benign. Colonoscopy revealed no abnormality in 30 of 46 patients with CRWT as a solitary finding, and revealed some abnormality in 16 patients (12 had diverticular disease, four had benign neoplastic lesions). CONCLUSIONS: CRWT as an incidental and solitary finding on CT should not be regarded as a pathology prompting a colonoscopy. Approximately two-thirds of the patients had a normal colonoscopy and the remaining patients had benign lesions (12 had diverticular disease and four had benign neoplastic lesions). However, many of these patients seem to warrant colonoscopy regardless of CT findings, particularly patients who have a family history of colorectal cancer, have positive fecal occult blood test results or who are older than 50 years of age.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.234
Threshold uncertainty score0.752

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.014
GPT teacher head0.229
Teacher spread0.215 · 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 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

Citations21
Published2008
Admission routes1
Has abstractyes

Explore more

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