ArticleRisk management and healthcare policy2026
A Simplified Nomogram for Primary Population-Based Screening of Esophageal Cancer: An Internally Validated Shandong Cohort Study Addressing Data Imbalance.
Article in Risk management and healthcare policy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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
Objective: To develop and validate a user-friendly nomogram for predicting the risk of esophageal squamous cell carcinoma (ESCC) and high-grade intraepithelial neoplasia (HGIN), designed for initial screening settings while addressing variable complexity and class imbalance in traditional models. Methods: Based on a screening cohort of 23,257 participants from Tai'an, Shandong, between 2024 and 2025 (positive rate: 1.54%), a 1:10 case-control sampling method was applied to address the low event rate (positive rate: 1.54%) and correct class imbalance. Predictors were initially screened using LASSO regression with 10-fold cross-validation (λ.min criterion) and further refined via multivariable logistic regression to establish the final model, which was presented as a nomogram and evaluated via internal split-sample validation. Results: Seven easily accessible predictors were identified: age, sex, education level, BMI, smoking history, hot-food consumption, and family history of esophageal cancer. The model showed strong discriminatory performance, with an AUC of 0.823 (95% CI: 0.798-0.848) in the training set and 0.835 (95% CI: 0.805-0.865) in the internal validation set. Calibration curves indicated high consistency between predicted and observed risks. Decision curve analysis demonstrated net clinical benefit across risk thresholds of 0-0.6. Conclusion: The proposed simplified nomogram demonstrates promising potential for risk stratification in primary ESCC/HGIN screening. However, prospective external validation in diverse cohorts is necessary before its large-scale clinical implementation.
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.