MétaCan
Menu
Back to cohort
Record W152204128 · doi:10.1155/2008/761208

Fecal DNA Screening in Colorectal Cancer

2008· review· en· W152204128 on OpenAlexaffvenueabout
Suzanne Richter

Bibliographic record

VenueCanadian Journal of Gastroenterology · 2008
Typereview
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsWestern University
Fundersnot available
KeywordsFecal occult bloodColorectal cancerMedicineFecesDiseaseInternal medicineColorectal cancer screeningIntensive care medicineCancerGold standard (test)OncologyTest (biology)ColonoscopyGastroenterologyBiology

Abstract

fetched live from OpenAlex

Colorectal cancer (CRC) is the third most common type of cancer diagnosed in Canada, and is the leading cause of cancer-related deaths in nonsmokers. Although CRC is considered to be 90% curable if detected early, the majority of patients present with advanced stage III or IV disease. An effective screening test may significantly decrease disease burden. The present paper examines the rationale and potential of fecal DNA testing as an alternative and adjunct to other CRC screening tests. The most efficacious fecal DNA test developed to date has a sensitivity and specificity of 87.5% and 82%, respectively. The approach has a higher positive predictive value than the currently used fecal occult blood test and offers a noninvasive option to patients. It is not reliant on the presence of bleeding, which may be intermittent or altogether absent. The test is now commercially available and is supported by a number of American insurers. Current challenges include cost reduction and demonstration of mortality benefit in a rigorous clinical trial. Despite current challenges, fecal DNA testing is worth pursuing. Both the American Gastroenterological Society and the American Cancer Society maintain that molecular testing is in its infancy but is promising. Fecal DNA testing has the potential to be an exciting addition to the current armament of CRC screening options.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.948
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
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 head0.039
GPT teacher head0.308
Teacher spread0.268 · 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 designOther design
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

Citations13
Published2008
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of GastroenterologySame topicColorectal Cancer Screening and DetectionFrench-language works237,207