ArticleJournal of endocrinological investigation2023
A nomogram and risk classification system for predicting cancer-specific survival in tall cell variant of papillary thyroid cancer: a SEER-based study.
Article in Journal of endocrinological investigation, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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
7 citing papers in PubMed.
- A clinical feature-based nomogram for the personalized prediction of radioactive iodine therapy outcomes in patients with papillary thyroid carcinoma.Oncology letters · 2025Article
- Racial and ethnic disparities in the risk of second primary malignancies in differentiated thyroid cancer patients: a population-based study.Endocrine · 2025Article
- Dynamic survival outcomes of the tall-cell variant of papillary thyroid carcinoma patients after surgery.Frontiers in endocrinology · 2025Article
- Article
- Article
- Development of a predictive nomogram for intermediate-risk differentiated thyroid cancer patients after fixed 3.7GBq (100mCi) radioiodine remnant ablation.Frontiers in endocrinology · 2024Article
- Risk factors and a prognostic model for patients with borderline resectable locally advanced T3-4N0-1 non-small cell lung cancer: a population-based study.Translational cancer research · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
backgroundTall cell variant (TCV) of papillary thyroid cancer (PTC) is the most common aggressive subtype of PTC. The factors that affect survival of patients with TCV remain unclear. We aimed to develop a model to predict the cancer-specific survival (CSS).
methodsA total of 1615 patients diagnosed with TCV between 2004 and 2016 were identified from the Surveillance, Epidemiology, and End Results (SEER) database and randomized into training and validation cohorts (7:3). A predictive nomogram for predicting CSS was constructed by Cox proportional hazards regression and validated by concordance index (C-index), calibration curve, and decision curve analyses (DCA). A risk classification system was built based on the total nomogram scores of each case.
resultsA nomogram was constructed including five independent prognostic factors (age, tumor size, T stage, M stage, and extent of surgery) associated with CSS in TCV patients. Various validations proved that the nomogram model had good consistency and discrimination for TCV prognosis. The risk classification system could perfectly classify TCV patients into three risk groups with significantly different CSS. Compared with traditional AJCC TNM staging system, the nomogram could better predict CSS in TCV patients.
conclusionsA nomogram and corresponding risk classification system were developed for predicting CSS in TCV patients. The model has excellent performance and can be used to help clinicians make accurate prognostic assessment and individualized treatment.
Indexed as
Identifiers
36376545What 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.