Simulation Models for Bladder Cancer: A Scoping Review
Bibliographic record
Abstract
Objectives: The study identifies and summarizes information from manuscripts using simulation models for Bladder Cancer (BCA) research. Methods: We conducted and presented results of a systematic literature search of Medline, Web of Science, and Google scholar, following the PRISMA guidelines for scoping reviews. We summarized extracted key components of the methodology, data sources, and software used for the development of simulation models and classify eligible articles in terms of the study objectives and conclusions. Results: The 97 identified modeling studies simulating aspects of BCA included models that (1) describe the biological process of carcinogenesis and tumor progression (mostly compartmental models); (2) examine the impact of screening protocols and interventions on disease progression and prognosis (mostly microsimulation models); and (3) assess the cost-effectiveness of BCA treatment and control strategies (cohort-based simulation models or simpler decision tree structures). The scope, objectives, and conclusions of these studies varied substantially. Most focused on evaluating treatments, mostly for non-muscle invasive bladder cancer, with some examining BCA screening and surveillance. Their objectives, methods, and analyses were inconsistently and often incompletely reported. Conclusions: Simulation models in bladder cancer examine questions that span the range from tumor kinetics to cost effectiveness of tumor management, but shortcomings in their reporting hinder assessments of their applicability and methodological rigor, severely limiting their practical usefulness. Highlight statements: We assessed the available landscape of simulation modeling for health decision making in BCA research.Shortcomings in the reporting of this research severely limit their practical usefulness.Future population modeling should assess BCA screening and surveillance. Strengths: This is the first, to our knowledge, systematic appraisal of simulation models in bladder cancer. Simulation modeling will be a key technology to assess the utility of highly promising novel diagnostics and treatments, while evidence accumulates.The described variation in the objectives, methodological rigor, and reporting of models' development, validation, and analysis likely generalize to other disease areas. Limitations: This descriptive compendium does not explicitly compare the results of different models between them or with observed data.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".