ArticleHeliyon2024
Predicting factors for extremity fracture among border-fall patients using machine learning computing.
Article in Heliyon, 2024. 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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The factors causing the injuries sustained from falls at US-Mexican border include falls from border wall or fence, fleeing from border patrols, ejecting from vehicle, and others. This study aimed to determine the factors leading to anatomical injuries and to identify the importance of factors leading to limb fracture and internal organ injuries. Methods: A total of 178 patients who sustained musculoskeletal injuries or internal organ injuries and were admitted to our hospital were included in this retrospective study. Factors indexed for analysis included demographics, comorbidities, and falling mechanic factors. Correlations between anatomical injuries and mechanical injuries were analyzed. Multilayer perceptron neural network (MPNN) was used to identify predictive factors and to stratify the importance of these factors leading to injuries. The SPSS software was used for statistical analysis and predictive factor analysis. Results: The extremity fracture was associated with border wall/fence fall (p = 0.001) and fleeing (p = 0.002). The spine fracture was correlated with bridge jump/fall (p = 0.007), fence jump/fall (p = 0.026). The vehicle ejecting/MVA was correlated with head injury (P < 0.001), chest injury (P < 0.001), and abdominal injury p < 0.001). MNPP stratify the importance of factor causing injury with multiple factor considered. Conclusion: The various injury factors caused different anatomical injuries. Multifactorial assessment associated with these injuries can improve the accuracy of diagnosis and develop a predictive model for clinical applications.
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.