ArticleSpinal cord2024
Development and validation of a novel screening tool for deep vein thrombosis in patients with spinal cord injury: A five-year cross-sectional study.
Article in Spinal cord, 2024. 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.
- Analysis of risk factors associated with pulmonary embolism in patients with spinal cord injury: A meta-analysis.Clinics (Sao Paulo, Brazil) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
STUDY
designCross-sectional study.
objectivesDeep vein thrombosis (DVT) presents a significant risk of complication in patients with spinal cord injury (SCI), necessitating accurate screening methods. While the Caprini Risk Assessment Model (Caprini RAM) has seen extensive use for DVT screening, its efficacy remains under scrutiny.
settingFirst Affiliated Hospital of China University of Science and Technology.
methodsWe created and evaluated three nomograms for their effectiveness in DVT screening. Model 1 incorporated variables such as age, D-dimer level, red blood cell (RBC) counts, platelet counts, presence of type 2 diabetes mellitus, high blood pressure, mode and level of injury, degree of impairments, and Caprini scores. Model 2 was derived from Caprini scores alone, and Model 3 focused on independent risk factors. We assessed these models using the area under the curve (AUC) of the receiver operating characteristic (ROC), calibration curves, and decision curve analysis (DCA), employing bootstrap resampling tests (500 iterations) to determine their accuracy, discriminative ability, and clinical utility. Internal validation was performed on a separate cohort. Nomogram was established with well-fitted calibration curves for model 1, 2 and 3(AUC = 0.808, 0.751 and 0.797; 95%CI = 0.76-0.86, 0.70-0.80 and 0.75-0.84; respectively), indicating model 1 outperformed the others in prediction DVT risk, followed by model 3 and 2. These findings were consistent in the validation cohort, with DCA further corroborating our conclusions.
conclusionA nomogram integrating clinical data with Caprini RAM provides a superior option for DVT screening in SCI patients within rehabilitation settings, outperforming Caprini RAM.
Indexed as
Identifiers
38997421What 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.