ArticleDigital health
Establishment and verification of the model in diagnosis of thalassemia trait based on red blood cell parameters: A two-center retrospective study.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Development and Internal Validation of a Machine Learning-Based Model for Thalassemia Identification Using Complete Blood Count Parameters.Diagnostics (Basel, Switzerland) · 2026Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: Thalassemia trait (TT) screening in resource-limited settings is hampered by reliance on expensive and complex tests. This study aimed to develop and validate a highly accessible machine learning-based tool using only routine blood parameters to accurately differentiate TT from non-TT and its major subtypes. Methods: The retrospective study included 987 individuals (221 α-TT, 211 β-TT and 555 non-TT) from two medical centers. Seven machine learning methods-Logistic Regression, Gaussian Naive Bayes, Decision Tree, Random Forest, Multilayer Perceptron, XGBoost, and CatBoost-were employed to develop diagnostic models, which were evaluated using accuracy, sensitivity, specificity, AUC, PPV, NPV, and F1 score. Results: The CatBoost model emerged as superior for differentiating TT from non-TT, achieving an AUC of 0.976, accuracy of 0.940, and specificity of 0.981. It also outperformed other models in distinguishing α-TT from β-TT (AUC = 0.842). Critically, this high-performance model was successfully deployed as a user-friendly WeChat mini-program AI Lab, for real-world clinical application. Conclusion: The deployed ML-based AI Lab represents a robust, interpretable, and scalable tool poised to enhance TT screening efficiency and accessibility, particularly in underserved healthcare environments.
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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 Socratic graph.