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Record W2810502634 · doi:10.1158/1538-7445.am2018-2213

Abstract 2213: A simple risk prediction model for high-risk adenomatous polyps at the time of colonoscopy

2018· article· en· W2810502634 on OpenAlexaffabout
Devon J. Boyne, Lisa M. Lix, Susanna Town, Steven J. Heitman, Robert J. Hilsden, Darren R. Brenner

Bibliographic record

VenueCancer Research · 2018
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsUniversity of ManitobaUniversity of Calgary
Fundersnot available
KeywordsMedicineColonoscopyColorectal cancerLogistic regressionFamily historyBody mass indexInternal medicinePopulationCancerPhysical therapy

Abstract

fetched live from OpenAlex

Abstract Introduction: The prediction of high risk adenoma polyp (HRAP) may help to prioritize the urgency and guide the performance of colonoscopy procedures. Our objective was to develop and internally validate a simple, scalable clinical prediction model. Methods: The study population consisted of 2,364 individuals aged 50 to 74 with no prior history of cancer who had a screening colonoscopy at the Forzani and MacPhail Colon Cancer Screening Centre in Calgary, Canada. A total of 190 HRAPs were identified (8.0%). A multivariable logistic regression model was created using colorectal cancer risk factors identified from prior research. Predictor variables were collected from a baseline health questionnaire and included patient demographic (age, sex, and ethnicity), lifestyle (body mass index, alcohol (daily vs. no), smoking (never vs. ever), physical activity (high vs. moderate to low), and non-steroidal anti-inflammatory drug (NSAID) use (yes vs. no)), medical (family history of colorectal cancer, personal history of diabetes, or fecal occult blood test within the past two years), and female-specific characteristics (menopausal status and hormone replacement therapy (yes vs. no)). The demographic variables were first used to create a baseline model and the benefits of adding the groups of lifestyle, medical, and female-specific variables into the model in various combinations was assessed. Five-fold internal cross validation was conducted. Performance was assessed using the C-statistic and Hosmer-Lemeshow goodness-of-fit test. Results: The average age of the participants was 58 years of whom 54.9% were male and 85.24% were Caucasian. The clinical prediction model included demographic and lifestyle variables. On average, the predicted probability of having a HRAP was 8.0% (IQR: 4.1% to 10.6%). The bias-adjusted C-statistic was 0.66 (95% CI: 0.62 to 0.70) and there was no evidence of a lack of calibration according to the Hosmer-Lemeshow goodness-of-fit test. The addition of the medical history variables (Δ AUC = +0.0001; p = 0.98) or female-specific variables (Δ AUC = + 0.006; p = 0.32) or both medical and female-specific groups of variables (Δ AUC = +0.004; p = 0.45) did not significantly improve predictive performance. Conclusions: A model based on demographic and lifestyle variables showed a modest predictive ability for having an HRAP at the time of colonoscopy in a population undergoing screening-related colonoscopies. Consideration of these factors may assist in guiding prioritization of limited screening resources. Next steps include external validation and testing the incremental predictive ability of circulating biomarkers. Citation Format: Devon J. Boyne, Lisa M. Lix, Susanna Town, Steven J. Heitman, Robert J. Hilsden, Darren R. Brenner. A simple risk prediction model for high-risk adenomatous polyps at the time of colonoscopy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2213.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.055
GPT teacher head0.384
Teacher spread0.330 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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".

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Citations1
Published2018
Admission routes2
Has abstractyes

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