MétaCan
Menu
Back to cohort
Record W2063340092 · doi:10.15273/dmj.vol40no2.4534

The Role of Computed Tomographic Colonography in Colorectal Cancer Screening

2014· article· en· W2063340092 on OpenAlexaffvenueabout
André R. Maddison, Geoff Williams

Bibliographic record

VenueDalhousie Medical Journal · 2014
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsDalhousie University
Fundersnot available
KeywordsColonoscopyMedicineColorectal cancerComputed tomographicRadiologyVirtual colonoscopyMedical physicsMEDLINEColorectal PolypSAFERCancerInternal medicineComputed tomographyComputer science

Abstract

fetched live from OpenAlex

Objective: We conducted a literature review to identify the current state of knowledge regarding the optimal clinical use of computed tomographic colonography (CTC) in Canada, based on accuracy, patient safety, and costeffectiveness. Methods: Articles were retrieved from PubMed and the Cochrane Library. Retrieved studies were included based on relevance and appropriateness as determined by reviewing titles and abstracts. Studies were excluded if they were duplicated, grey literature, or non-peer-reviewed. Of the studies remaining after exclusions, reference lists were scanned to obtain further relevant articles. Results: The literature reports comparable accuracy for detecting cancers and large polyps, yet CTC is less sensitive than colonoscopy for detecting small polyps. Most would agree that CTC is safer than colonoscopy, yet it is not without risk or adverse events. Lastly, although the true costs of CTC vs. colonoscopy are complex, the literature consistently demonstrates that CRC screening with CTC is less cost-effective than screening with colonoscopy. Conclusion: Unless there are modifications to CTC that improve cost-effectiveness and/or accuracy, the future of CRC screening in Canada will remain reliant on colonoscopy. CTC is beneficial as an alternative to colonoscopy, but should remain available for selected indications. CTC has value, however, it has fallen short of initial expectations.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.717
Threshold uncertainty score0.469

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.008
GPT teacher head0.259
Teacher spread0.251 · 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

Citations0
Published2014
Admission routes3
Has abstractyes

Explore more

Same venueDalhousie Medical JournalSame topicColorectal Cancer Screening and DetectionFrench-language works237,207