ArticleFrontiers in endocrinology2025
Multimodal ultrasonographic and clinicopathological model for predicting high-volume lymph node metastasis in cN0 papillary thyroid carcinoma.
Article in Frontiers in endocrinology, 2025. 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
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
1 citing paper in PubMed.
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Given the challenge in preoperative diagnosis of high-volume lymph node metastasis (HVLNM) in clinical practice, we constructed and externally validated a comprehensive predictive model that integrated conventional ultrasound characteristics, contrast-enhanced ultrasound (CEUS) parameters, BRAF Methods: Totally, 126 clinically lymph node-negative (cN0) PTC patients who underwent subtotal or total thyroidectomy and accompanied with prophylactic cervical lymph node dissection between December 2022 and December 2024 were enrolled in this retrospective study, and an additional 47 cN0 PTC patients included for the external validation cohort. Univariate and multivariate analysis were performed to identify the independent risk factors for HVLNM, and a binary logistic regression equation and relevant nomogram was constructed to predict the risk about HVLNM. The model underwent internal validation using ten-fold cross-validation and further externally validated in an independent external cohort. Clinical practicality of the nomogram model was assessed by the area under the curve (AUC), calibration curve, and decision curve analysis (DCA). Results: Age, Dmax, ACR scores ≥11 points, and heterogeneous enhancement were four independent predictors of HVLNM after univariate and multivariate analysis in training set. These predictors were used to construct the corresponding nomograms with AUC of 0.860(95% CI: 0.792-0.928). Calibration curves and DCA plots revealed their robust calibration performances and fine net benefits. The accuracy and Kappa value obtained through ten-fold cross-validation were 0.864 and 0.468. The ROC value of the external validation was 0.885(95% CI:0.792-0.978). Conclusion: Our visualization nomogram provides clinicians with useful information in a simple and cost-effective manner, aiding in the formulation of personalized treatment plans and the reduction of reoperation rates.
Indexed as
Identifiers
What Socratic 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 Socratic graph.