ArticleFrontiers in oncology2023
Development and validation of a web-based predictive model for preoperative diagnosis of localized colorectal cancer and colorectal adenoma.
Article in Frontiers in oncology, 2023. 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Localized colorectal cancer (LCC) has obscure clinical signs, which are difficult to distinguish from colorectal adenoma (CA). This study aimed to develop and validate a web-based predictive model for preoperative diagnosis of LCC and CA. Methods: We conducted a retrospective study that included data from 500 patients with LCC and 980 patients with CA who were admitted to Dongyang People's Hospital between November 2012 and June 2022. Patients were randomly divided into the training (n=1036) and validation (n=444) cohorts. Univariate logistic regression, least absolute shrinkage and selection operator regression, and multivariate logistic regression were used to select the variables for predictive models. The area under the curve (AUC), calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC) were used to evaluate the performance of the model. Results: The web-based predictive model was developed, including nine independent risk factors: age, sex, drinking history, white blood cell count, lymphocyte count, red blood cell distribution width, albumin, carcinoembryonic antigen, and fecal occult blood test. The AUC of the prediction model in the training and validation cohorts was 0.910 (0.892-0.929) and 0.894 (0.862-0.925), respectively. The calibration curve showed good consistency between the outcome predicted by the model and the actual diagnosis. DCA and CIC showed that the predictive model had a good clinical application value. Conclusion: This study first developed a web-based preoperative prediction model, which can discriminate LCC from CA and can be used to quantitatively assess the risks and benefits in clinical practice.
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.