ArticleBMC cancer2026
Construction and validation of a predictive model for fear of progression among postoperative patients with breast cancer: a multi-center cross-sectional study.
Article in BMC cancer, 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
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundFear of progression (FoP) is a prevalent psychological response to the real threats associated with cancer diagnosis, treatment, and disease trajectory, and it represents one of the most prominent sources of distress symptoms among patients with breast cancer. Persistently elevated FoP may not only negatively impact treatment outcomes but also contribute to excessive healthcare utilization and reduced quality of life. This study aimed to construct and validate a predictive model for FoP in postoperative breast cancer patients based on the Health Ecology Model.
methodsThis multi-center cross-sectional study enrolled 347 postoperative breast cancer patients from three tertiary Grade-A general hospitals in Anhui Province, China, between March 25, 2024, and June 25, 2024. Univariable analysis and multivariable logistic regression were employed to identify independent predictors of FoP. A nomogram was then constructed based on these predictors and internally validated. Model performance was assessed in terms of discrimination, calibration, and clinical usefulness using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA), respectively.
resultsThe prevalence of high FoP was 52.74%. Multivariable logistic regression identified chemotherapy, frequency of physical activity, work status, sense of coherence, illness perception, and coping mode as independent predictors of FoP, which were incorporated into the nomogram. The model demonstrated excellent discriminatory power, with areas under the ROC curve (AUC) of 0.867 and 0.898 for the training and validation sets, respectively. Calibration plots indicated good agreement between predicted and observed outcomes, while DCA indicated favorable clinical utility across a range of threshold probabilities.
conclusionsA predictive model for FoP among postoperative breast cancer patients was successfully developed and validated within the HEM framework. This model may facilitate the early identification of individuals at high risk for FoP and provide a foundation for targeted interventions aimed at alleviating psychological distress and improving overall well-being.
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.