SynthesisJournal of gastroenterology and hepatology2024
Systematic review: risk prediction models for metachronous advanced colorectal neoplasia after polypectomy.
Synthesis in Journal of gastroenterology and hepatology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic review: risk prediction models for metachronous advanced colorectal neoplasia after polypectomy.Journal of gastroenterology and hepatology · 2024Pooled it
- Enhancing FIT-based colorectal cancer screening through risk prediction: a comparative evaluation of 17 models in a large Chinese cohort.Cancer biology & medicine · 2026Article
- Surveillance of colorectal neoplasia: a review of guidelines, current evidence, and future directions.Colorectal cancer · 2025Article
- Predictors of metachronous advanced colorectal adenoma after polypectomy.Journal of the Chinese Medical Association : JCMA · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
BACKGROUND AND
aimColorectal cancer (CRC) is the fourth leading cause of cancer death globally. CRC surveillance is a common indication for colonoscopy, representing a considerable burden for endoscopy services. Accurate identification of high-risk patients who would benefit from more intensive surveillance, as well as low-risk patients suitable for less frequent follow-up, could improve the effectiveness of surveillance protocols and resource use. Our aim was to identify and critically appraise published risk models for the occurrence of metachronous advanced colorectal neoplasia (ACN), defined here as CRC or advanced adenomas detected during surveillance colonoscopy.
methodsWe searched PubMed and EMBASE for primary research studies reporting the development and/or validation of multivariable models that predict metachronous ACN risk. Screening of studies for inclusion, data extraction, and risk of bias assessment were conducted by two researchers independently.
resultsWe identified nine studies describing nine risk models. Six models were internally validated and two were externally validated. No model underwent both internal and external validation. Good model discrimination (concordance index > 0.7) was reported for two models during internal validation and for one model during external validation. Calibration was acceptable when assessed (n = 4). Methodological limitations and a high risk of bias were observed for all studies.
conclusionsSeveral published models predicting metachronous ACN risk showed some promise. However, adherence to methodological standards was limited, and only two models were externally validated. Head-to-head comparisons of existing models using populations independent from model development cohorts should be prioritized to identify models suitable for use in clinical practice.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.