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Record W2068479860 · doi:10.5217/ir.2014.12.2.169

Interval Cancers after a Negative Colonoscopy Finding in a Korean Population: A Small Step for Gastroenterologists but One Giant Leap for Koreans

2014· article· en· W2068479860 on OpenAlexaboutno aff
Jae Myung

Bibliographic record

VenueIntestinal Research · 2014
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineColonoscopyIncidence (geometry)PopulationColorectal cancerEpidemiologyCancer registryReferralCancerObservational studyDemographyInternal medicineConfidence intervalFamily medicineEnvironmental health

Abstract

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The incidence of colorectal cancer (CRC) in Korea has increased markedly in recent years. This epidemiological change requires CRC screening to reduce the CRC incidence and CRC-related mortality in Korea. A number of observational studies have shown the risk for CRC to be low within the 10-year screening interval after a negative colonoscopy.1 Nevertheless, interval cancers occur, especially in the right colon.2 Most previous studies on interval cancers after colonoscopy relied on registry or administrative data from Canada or the United States,3,4,5,6,7 and the quality of colonoscopic data might differ between countries. Therefore, the prevalence and predictors of interval cancer in the Korean population have been undisclosed. In this regard, I read with great interest the study by Kim et al.,8 who are to be congratulated for their clinical study showing the prevalence, clinicopathological characteristics, and predictors of interval cancers in the Korean population. However, I have concerns about the study methodology and therefore the conclusions drawn. In the study by Kim et al.,8 the prevalence of interval cancer was 6.2% (30 cases among 482 patients). However, this result may be limited by referral, selection, and recall biases, as it was based on data obtained via telephone calls from a single tertiary referral center. In general, the prevalence of interval cancer may correctly be assessed by conducting a populationbased study, and the prevalence of interval cancer based on a population study in the West was 4.0%-7.9%.4,5,6,7,9,10 Kim et al.8 also suggested that young age and right-side location were independent factors associated with interval cancer in a multivariate analysis. In a recent study from Canada, however, female sex, older age, and performance of the colonoscopy by a non-gastroenterologist were identified as predictors of interval cancers after a negative colonoscopy.3,4 Although other authors suggested the accelerated tumor biology in young patients as a cause of interval cancer, the authors of the Canadian studies3,4 suggested that a deficiency in the quality of colonoscopic data rather than accelerated tumor biology was the cause of most of the interval cancers occurring after a negative colonoscopy. Furthermore, information on family CRC history or hereditary syndromes was not described by Kim et al.; therefore, their study might have been unable to evaluate the age impact on interval cancers, as young age in familial CRC may have a confounding effect on the interval cancer. As pointed out by the authors, it would be better if quality colonoscopic data such as bowel preparation, completeness, and adenoma detection rate, as well as qualifications of endoscopists, were assessed as predictors of the occurrence of interval cancers. Considering the wide variation in the detection rates of adenomas and serrated polyps between endoscopists, the quality of colonoscopic data should be stressed and assessed as a predictor of interval cancers. However, to answer these questions, a population-based cohort design with complete follow-up might be warranted. While we applaud the investigators for obtaining Korean data about interval cancer for the first time, which might be a small step for gastroenterologists but a giant leap for Koreans, methodological issues need to be addressed before conclusive results can be drawn from their study about the prevalence and predictors of interval cancers in the Korean population.

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)0.004
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 score0.423
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.128
GPT teacher head0.398
Teacher spread0.269 · 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

Citations2
Published2014
Admission routes1
Has abstractyes

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