SynthesisBMJ open2019
A systematic review of methodological quality of model development studies predicting prognostic outcome for resectable pancreatic cancer.
Synthesis in BMJ open, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
What it found
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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
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review.BMC medical research methodology · 2022Pooled it
- Identification and Validation of a Lipid Metabolism-Related Gene Signature for Predicting Prognosis and Immunotherapy Response in Oral Squamous Cell Carcinoma.Metabolites · 2026Article
- The reporting quality and methodological quality of dynamic prediction models for cancer prognosis.BMC medical research methodology · 2025Article
- Machine Learning Models for Predicting Mortality in Critically Ill Patients with Sepsis-Associated Acute Kidney Injury: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Glioblastoma glycolytic signature predicts unfavorable prognosis, immunological heterogeneity, and ENO1 promotes microglia M2 polarization and cancer cell malignancy.Cancer gene therapy · 2023Article
- Risk of bias of prognostic models developed using machine learning: a systematic review in oncology.Diagnostic and prognostic research · 2022Article
- Structured reporting to improve transparency of analyses in prognostic marker studies.BMC medicine · 2022Review
- Review
- Promoting Prognostic Model Application: A Review Based on Gliomas.Journal of oncology · 2021Review
- C-Reactive Protein and Neutrophil/Lymphocytes Ratio: Prognostic Indicator for Doubling overall survival Prediction in Pancreatic Cancer Patients.Journal of clinical medicine · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo assess the methodological quality of prognostic model development studies pertaining to post resection prognosis of pancreatic ductal adenocarcinoma (PDAC). DESIGN/
settingA narrative systematic review of international peer reviewed journals DATA SOURCE: Searches were conducted of: MEDLINE, Embase, PubMed, Cochrane database and Google Scholar for predictive modelling studies applied to the outcome of prognosis for patients with PDAC post resection. Predictive modelling studies in this context included prediction model development studies with and without external validation and external validation studies with model updating. Data was extracted following the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) checklist. PRIMARY AND SECONDARY OUTCOME MEASURES: Primary outcomes were all components of the CHARMS checklist. Secondary outcomes included frequency of variables included across predictive models.
results263 studies underwent full text review. 15 studies met the inclusion criteria. 3 studies underwent external validation. Multivariable Cox proportional hazard regression was the most commonly employed modelling method (n=13). 10 studies were based on single centre databases. Five used prospective databases, seven used retrospective databases and three used cancer data registry. The mean number of candidate predictors was 19.47 (range 7 to 50). The most commonly included variables were tumour grade (n=9), age (n=8), tumour stage (n=7) and tumour size (n=5). Mean sample size was 1367 (range 50 to 6400). 5 studies reached statistical power. None of the studies reported blinding of outcome measurement for predictor values. The most common form of presentation was nomograms (n=5) and prognostic scores (n=5) followed by prognostic calculators (n=3) and prognostic index (n=2).
conclusionsAreas for improvement in future predictive model development have been highlighted relating to: general aspects of model development and reporting, applicability of models and sources of bias. TRIAL REGISTRATION NUMBER: CRD42018105942.
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