SynthesisFrontiers in public health2021
Risk Prediction Model for Esophageal Cancer Among General Population: A Systematic Review.
Synthesis in Frontiers in public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.
- Diagnostic Risk Prediction Models for Upper Gastrointestinal Cancers: A Systematic Review.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2025Pooled it
- Positive effects of a perioperative training intervention in Ivor Lewis oesophageal surgery: a randomised, controlled multicentre trial.BMC surgery · 2025Trial
- DDR1 in esophageal squamous cell carcinoma: bioinformatics discovery and experimental validation.Translational cancer research · 2026Article
- Plant-based diet quality is associated with esophageal mucosa-associated microbiome profiles and disease severity in a high-risk Chinese population.Journal of oral microbiology · 2026Article
- Explainable machine learning for early diagnosis of esophageal cancer: A feature-enriched Light Gradient Boosting Machine framework with Shapley Additive Explanations and Local Interpretable Model-Agnostic Explanations interpretations.The Journal of international medical research · 2026Article
- A Systematic Review of Risk Prediction Models for Esophageal Adenocarcinoma in the General Population.Gastro hep advances · 2025Review
- Non-invasive early detection on esophageal squamous cell carcinoma and precancerous lesions by microbial biomarkers combining epidemiological factors in China.Journal of gastroenterology · 2024Article
- Design of risk prediction model for esophageal cancer based on machine learning approach.Heliyon · 2024Article
- Perioperative immunotherapy for esophageal squamous cell carcinoma.Frontiers in immunology · 2024Review
- Development and validation of a novel risk prediction algorithm to estimate 10-year risk of oesophageal cancer in primary care: prospective cohort study and evaluation of performance against two other risk prediction models.The Lancet regional health. Europe · 2023Article
- Review
- Development and Validation of Esophageal Squamous Cell Carcinoma Risk Prediction Models Based on an Endoscopic Screening Program.JAMA network open · 2023Article
- lncRNA SSTR5-AS1 Predicts Poor Prognosis and Contributes to the Progression of Esophageal Cancer.Disease markers · 2023Article
- Tobacco Smoking and Gastrointestinal Cancer Risk.Visceral medicine · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
PubMed holds no abstract for this paper.
Indexed as
Identifiers
What OpenQuestion holds
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.