Evidence map›Paper›PMID 38800094›Full record

ArticleJournal of orthopaedic translation2024

Global, regional, national trends of femur fracture and machine learning prediction: Comprehensive findings and questions from global burden of disease 1990-2019.

Jinyi Wu, Yanjun Che, Yue Zhang, Junwen Wang, Ming Chen, Jun Jiang, Qingwu Jiang, Yibiao Zhou

Abstract read
In one paragraph

Article in Journal of orthopaedic translation, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
37citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

37 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
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  4. Observational
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  7. [Analysis of disease burden of fall-related fractures among elderly Chinese from 1990 to 2023].Zhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery · 2026
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  12. Disease Burden Attributable to Hip Fractures in Asian Women.International journal of women's health · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Jinyi WuFudan University School of Public Health, Xuhui, Shanghai, China.
Yanjun CheOrthopedics and Sports Medicine Center, Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou 215008, Jiangsu Province, China.
Yue ZhangSchool of Public Health, Department of Epidemiology, Shanxi Medical University, Key Laboratory of Coal Environmental Pathogenicity and Prevention, Ministry Education, Taiyuan, 030001, China.
Junwen WangWuhan fourth hospital, Qiaokou, Wuhan, China.
Ming ChenWuhan fourth hospital, Qiaokou, Wuhan, China.
Jun JiangFudan University School of Public Health, Xuhui, Shanghai, China.
Qingwu JiangFudan University School of Public Health, Xuhui, Shanghai, China.
Yibiao ZhouFudan University School of Public Health, Xuhui, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Femur fracture is a type of fracture with high disability and mortality. There is no comprehensive analysis and prediction of the global distribution of femur fractures, so we conducted this study. Methods: Age-standardized incidence rate (ASIR), age-standardized prevalence rate (ASPR), and years living with disability (YLDs) of femur fractures (excluding femoral neck) were downloaded from the Global burden of disease database. Trend analysis was performed, and 6 time-series machine learning algorithms were applied to predict the global ASIR, ASPR, and YLDs. Results: ASPR for femur fracture had been increasing in most countries worldwide from 1990 to 2019, with the highest in East Asia (AAPC = 1.25 95%Confidence Interval (1.2, 1.3)) and lowest in Central Latin America (AAPC = -0.74 95%CI (-0.81, -0.67)). However, ASIR showed a significant downward trend worldwide, with East Saharan Africa decreasing the most (AAPC = -4.04 95%CI (-5.56, -2.47)), and East Asia elevating the most (AAPC = 1.11 95%CI (0.87, 1.42)). YLDs were increasing over the world, with East Asia still elevating the most AAPC= (3.9 95%CI (3.85, 3.95)), with the only region of decrease being Eastern Europe (AAPC = -0.28 95%CI (-0.3, -0.26)). Both ASPR and ASIR were higher in women than in men in the >75 year group, whereas YLDs was lower in women than in men in the >60 year group. Globally, the ARIMA model was optimal in the prediction of ASPR, the PROPHET model effected in the prediction of ASIR, and the PROPHET WITH XGBOOST model was the best in the prediction of YLDs. The projections showed increase in both ASPR and YLDs, except for ASIR decreasing by 2030. Conclusions: Our study found a rise in femur fracture ASPR and ASIR from 1990 to 2019 in war conflict areas and East Asia, meanwhile, the YLDs of femur fracture increased in populous countries. In both 1990 and 2019, both ASPR and ASIR were higher in women over 75 years than that in men, but YLDs was higher in men over 60 years than that in women. In 2020-2030, while global femur fracture ASIR might decline, both ASPR and YLDs might rise. The Translational Potential of this article: Femur fracture is a high-energy injury due to direct violence, and in war, conflicting and underdeveloped regions such as East Asia. Accidental injuries may occur due to the rapid development of industry and the frequent traffic accidents. This study suggests that we should focus on elderly women (≥75 years) in the above regions in the future. For older men (>60 years old), more attention should be paid to post-fracture functional rehabilitation and early reintegration into society to reduce the disability rate and lower the socio-economic burden.

Indexed as

Femur fractureGBDMachine learningPredictionVisualization

Identifiers

PMID38800094
PMCPMC11127185

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.