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Record W4402841625 · doi:10.5463/thesis.818

Mathematical modelling to guide colorectal cancer screening and surveillance policies

2024· dissertation· en· W4402841625 on OpenAlexaboutno aff
Francine van Wifferen

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsnot available
Fundersnot available
KeywordsColorectal cancerColorectal cancer screeningComputer scienceMedicineCancerInternal medicineColonoscopy

Abstract

fetched live from OpenAlex

This thesis consists of three main parts, each addressing different topics regarding the evaluation and improvement of CRC screening and surveillance. In the first part of this thesis, we describe possibilities to improve the CRC screening programme. In Chapter 2, we study the benefit-harm balance of participating in CRC screening for many subgroups aiming to help individuals making an informed decision about screening participation. To do so, we combine risk estimates of the benefits and harms of CRC screening, derived with the ASCCA model, with the relative importance of CRC screening outcomes obtained with a preference eliciting survey in order to obtain the benefit-harm balance of screening. In Chapter 3, we evaluate the clinical utility of a new stool test in a large-scale paired-intervention study conducted in the Dutch CRC screening programme. In Chapter 4, we assess the accuracy of various summarising measures commonly used to report adherence over multiple rounds of stool-based CRC screening. In addition, we assess the impact of using these summarising measures, rather than using detailed longitudinal adherence data, on model-predicted CRC screening effectiveness using the ASCCA model. The second part of this thesis investigates surveillance in two populations who are at increased risk of CRC. In Chapter 5, we evaluate whether stool-based surveillance could serve as an alternative to colonoscopy surveillance in a post-polypectomy surveillance population. In Chapter 6, we study the optimal surveillance strategy for individuals with a family history of CRC, considering colonoscopy surveillance, FIT-based surveillance and surveillance including both colonoscopy and FIT. The third part of this thesis focuses on the impact of the COVID-19 pandemic on CRC screening programmes. For this purpose, multiple independent models are used to answer the same research question. In addition to the ASCCA model, the MISCAN-Colon model, the Policy1-Bowel model, and the OncoSim model are used, which are developed for the Netherlands, Australia, and Canada, respectively. We study the short-term and long-term impact of hypothetical disruptions to CRC screening programmes in three countries, e.g. the Netherlands, Australia and Canada, in Chapter 7. In Chapter 8, we investigate two approaches for managing the screening backlog that results from a three-month screening disruption in the same three countries. The objective is to provide guidance on how to manage catch-up screening within available colonoscopy capacity. In Chapter 9, we estimate the global impact on CRC burden due to COVID-19 related decreases to organised CRC screening based on real-world data. To do so, all four models are used to draw conclusions for countries other than those for which the model was originally developed. As this provides an additional level of uncertainty, the results are aggregated across the multiple models to take this uncertainty into account. Chapter 10 summarizes the main findings presented in this thesis. Moreover, we discuss methodological issues and provide recommendations for future research.

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)0.003
metaresearch head score (Gemma)0.018
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: none
Teacher disagreement score0.015
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0150.002

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 head0.026
GPT teacher head0.332
Teacher spread0.306 · 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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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