SynthesisBMC cancer2020
Development, validation and effectiveness of diagnostic prediction tools for colorectal cancer in primary care: a systematic review.
Synthesis in BMC cancer, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 3 of them syntheses 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
13 citing papers in PubMed, 3 syntheses or guidelines pooled it, 24 citations in OpenAlex.
- [Cancer prevention recommendations: Update 2024].Atencion primaria · 2024Guideline
- Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review.BMC medical research methodology · 2022Pooled it
- Risk prediction models for symptomatic patients with bladder and kidney cancer: a systematic review.The British journal of general practice : the journal of the Royal College of General Practitioners · 2022Pooled it
- A machine learning tool for identifying metastatic colorectal cancer in primary care.Scandinavian journal of primary health care · 2025Article
- GastricAITool: A Clinical Decision Support Tool for the Diagnosis and Prognosis of Gastric Cancer.Biomedicines · 2024Article
- Validation of a diagnostic prediction tool for colorectal cancer: a case-control replication study.Family practice · 2023Article
- A Scoring Method to Prioritize Fecal Occult Blood Testing as a First Step in Colorectal Cancer Screening in Resource-Limited Settings.Diagnostics (Basel, Switzerland) · 2023Article
- Using an electronic safety netting tool designed to improve safety with respect to cancer referral in primary care: a qualitative service evaluation using rapid appraisal methods.BMJ open quality · 2023Article
- Machine Learning in Colorectal Cancer Risk Prediction from Routinely Collected Data: A Review.Diagnostics (Basel, Switzerland) · 2023Review
- [PAPPS Expert Groups. Cancer prevention recommendations: Update 2022].Atencion primaria · 2022Article
- Risk of bias of prognostic models developed using machine learning: a systematic review in oncology.Diagnostic and prognostic research · 2022Article
- Faecal immunochemical test outside colorectal cancer screening?World journal of gastroenterology · 2021Review
- Recognising Colorectal Cancer in Primary Care.Advances in therapy · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundTools based on diagnostic prediction models are available to help general practitioners (GP) diagnose colorectal cancer. It is unclear how well they perform and whether they lead to increased or quicker diagnoses and ultimately impact on patient quality of life and/or survival. The aim of this systematic review is to evaluate the development, validation, effectiveness, and cost-effectiveness, of cancer diagnostic tools for colorectal cancer in primary care.
methodsElectronic databases including Medline and Web of Science were searched in May 2017 (updated October 2019). Two reviewers independently screened titles, abstracts and full-texts. Studies were included if they reported the development, validation or accuracy of a prediction model, or assessed the effectiveness or cost-effectiveness of diagnostic tools based on prediction models to aid GP decision-making for symptomatic patients presenting with features potentially indicative of colorectal cancer. Data extraction and risk of bias were completed by one reviewer and checked by a second. A narrative synthesis was conducted.
resultsEleven thousand one hundred thirteen records were screened and 23 studies met the inclusion criteria. Twenty-studies reported on the development, validation and/or accuracy of 13 prediction models: eight for colorectal cancer, five for cancer areas/types that include colorectal cancer. The Qcancer models were generally the best performing. Three impact studies met the inclusion criteria. Two (an RCT and a pre-post study) assessed tools based on the RAT prediction model. The third study looked at the impact of GP practices having access to RAT or Qcancer. Although the pre-post study reported a positive impact of the tools on outcomes, the results of the RCT and cross-sectional survey found no evidence that use of, or access to, the tools was associated with better outcomes. No study evaluated cost effectiveness.
conclusionsMany prediction models have been developed but none have been fully validated. Evidence demonstrating improved patient outcome of introducing the tools is the main deficiency and is essential given the imperfect classification achieved by all tools. This need is emphasised by the equivocal results of the small number of impact studies done so far.
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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.