SynthesisBioMed research international2019
Prognostic Models for Predicting Overall Survival in Patients with Primary Gastric Cancer: A Systematic Review.
Synthesis in BioMed research international, 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
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
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning methods for functional recovery prediction and prognosis in post-stroke rehabilitation: a systematic review.Journal of neuroengineering and rehabilitation · 2022Pooled it
- Machine learning in gastrointestinal endoscopy: challenges and opportunities.BMJ open gastroenterology · 2025Review
- The reporting quality and methodological quality of dynamic prediction models for cancer prognosis.BMC medical research methodology · 2025Article
- Clinical prediction models using machine learning in oncology: challenges and recommendations.BMJ oncology · 2025Review
- Development and validation of a Log odds of negative lymph nodes/T stage ratio-based prognostic model for gastric cancer.Frontiers in oncology · 2025Article
- MHAttnSurv: Multi-head attention for survival prediction using whole-slide pathology images.Computers in biology and medicine · 2023Article
- Review
- Prediction of long-term survival after gastrectomy using random survival forests.The British journal of surgery · 2021Article
- Article
- LINC01272 Promotes Migration and Invasion of Gastric Cancer Cells via EMT.OncoTargets and therapy · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study was designed to review the methodology and reporting of gastric cancer prognostic models and identify potential problems in model development.
methodsThis systematic review was conducted following the CHARMS checklist. MEDLINE and EMBASE were searched. Information on patient characteristics, methodological details, and models' performance was extracted. Descriptive statistics was used to summarize the methodological and reporting quality.
resultsIn total, 101 model developments and 32 external validations were included. The median (range) of training sample size, number of death, and number of final predictors were 360 (29 to 15320), 193 (14 to 9560), and 5 (2 to 53), respectively. Ninety-one models were developed from routine clinical data. Statistical assumptions were reported to be checked in only nine models. Most model developments (94/101) used complete-case analysis. Discrimination and calibration were not reported in 33 and 55 models, respectively. The majority of models (81/101) have never been externally validated. None of the models have been evaluated regarding clinical impact.
conclusionsMany prognostic models have been developed, but their usefulness in clinical practice remains uncertain due to methodological shortcomings, insufficient reporting, and lack of external validation and impact studies. IMPACT: Future research should improve methodological and reporting quality and emphasize more on external validation and impact assessment.
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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.