|
1
|
Zrour S, Belhaj Salem S, Ben Chekaya N,
Grassa R, Bejia I and Abid A: Survival rate after osteoporotic
proximal femur fractures. Tunis Med. 102:1078–1083. 2024.PubMed/NCBI View Article : Google Scholar : (In French).
|
|
2
|
Andaloro S, Cacciatore S, Risoli A, Comodo
RM, Brancaccio V, Calvani R, Giusti S, Schlögl M, D'Angelo E,
Tosato M, et al: Hip fracture as a systemic disease in older
adults: A narrative review on multisystem implications and
management. Med Sci (Basel). 13(89)2025.PubMed/NCBI View Article : Google Scholar
|
|
3
|
Fa-Binefa M, Clara A, Lamas C and Elosua
R: Mediterranean diet and risk of hip fracture: A systematic review
and dose-response meta-analysis. Nutr Rev. 83:1133–1143.
2025.PubMed/NCBI View Article : Google Scholar
|
|
4
|
Soro-García P and González-Gálvez N:
Effects of progressive resistance training after hip fracture: A
systematic review. J Funct Morphol Kinesiol. 10(54)2025.PubMed/NCBI View Article : Google Scholar
|
|
5
|
Geerts WH, Bergqvist D, Pineo GF, Heit JA,
Samama CM, Lassen MR and Colwell CW: Prevention of venous
thromboembolism: American college of chest physicians
evidence-based clinical practice guidelines (8th edition). Chest.
133 (Suppl 6):381S–453S. 2008.PubMed/NCBI View Article : Google Scholar
|
|
6
|
Zöller B, Li X, Sundquist J and Sundquist
K: Risk of pulmonary embolism in patients with autoimmune
disorders: A nationwide follow-up study from Sweden. Lancet.
379:244–249. 2012.PubMed/NCBI View Article : Google Scholar
|
|
7
|
Anderson FA Jr and Spencer FA: Risk
factors for venous thromboembolism. Circulation. 107 (Suppl
1):I9–I16. 2003.PubMed/NCBI View Article : Google Scholar
|
|
8
|
Zhang L, He M, Jia W, Xie W, Song Y, Wang
H, Peng J, Li Y, Wang Z and Lin Z: Analysis of high-risk factors
for preoperative DVT in elderly patients with simple hip fractures
and construction of a nomogram prediction model. BMC Musculoskelet
Disord. 23(441)2022.PubMed/NCBI View Article : Google Scholar
|
|
9
|
Piazza G, Goldhaber SZ, Kroll A, Goldberg
RJ, Emery C and Spencer FA: Venous thromboembolism in patients with
diabetes mellitus. Am J Med. 125:709–716. 2012.PubMed/NCBI View Article : Google Scholar
|
|
10
|
Ageno W, Becattini C, Brighton T, Selby R
and Kamphuisen PW: Cardiovascular risk factors and venous
thromboembolism: A meta-analysis. Circulation. 117:93–102.
2008.PubMed/NCBI View Article : Google Scholar
|
|
11
|
Yang CS and Tan Z: Construction and
validation of a predictive model for preoperative lower extremity
deep vein thrombosis risk in elderly hip fracture patients: An
observational study. Medicine (Baltimore).
103(e39825)2024.PubMed/NCBI View Article : Google Scholar
|
|
12
|
Caprini JA: Risk assessment as a guide for
thrombosis prophylaxis. Curr Opin Pulm Med. 16:448–452.
2010.PubMed/NCBI View Article : Google Scholar
|
|
13
|
Doggen CJM, Smith NL, Lemaitre RN,
Heckbert SR, Rosendaal FR and Psaty BM: Serum lipid levels and the
risk of venous thrombosis. Arterioscler Thromb Vasc Biol.
24:1970–1975. 2004.PubMed/NCBI View Article : Google Scholar
|
|
14
|
Moellmann HL, Alhammadi E, Boulghoudan S,
Kuhlmann J, Mevissen A, Olbrich P, Rahm L and Frohnhofen H: Risk of
sarcopenia, frailty and malnutrition as predictors of postoperative
delirium in surgery. BMC Geriatr. 24(971)2024.PubMed/NCBI View Article : Google Scholar
|
|
15
|
Niu Y, Wang Q, Lu J, He P and Guo HT: Risk
factors for postoperative delirium in orthopedic surgery patients:
A systematic review and meta-analysis. Ann Med.
57(2534520)2025.PubMed/NCBI View Article : Google Scholar
|
|
16
|
Ge X, Yao L, Liu Y, Wang Y and Zhang F:
Comparing machine learning models for predicting preoperative DVT
incidence in elderly hypertensive patients with hip fractures: A
retrospective analysis. Sci Rep. 15(13206)2025.PubMed/NCBI View Article : Google Scholar
|
|
17
|
Chen H, Yu D, Zhang J and Li J: Machine
learning for prediction of postoperative delirium in adult
patients: A systematic review and meta-analysis. Clin Ther.
46:1069–1081. 2024.PubMed/NCBI View Article : Google Scholar
|
|
18
|
Yuan J, Zeng Q, Li J, Cong Z and Zhang Y:
Machine learning applications in sports injury prediction: A
narrative review. Sci Prog. 108(368504251385956)2025.PubMed/NCBI View Article : Google Scholar
|
|
19
|
Rozera T, Pasolli E, Segata N and Ianiro
G: Machine learning and artificial intelligence in the multi-omics
approach to gut microbiota. Gastroenterology. 169:487–501.
2025.PubMed/NCBI View Article : Google Scholar
|
|
20
|
Ramos MV: Reviewing the context of
molecular modeling to enhance the application of machine learning
technologies for safer bioinformatics. Protein Pept Lett.
32:772–775. 2025.PubMed/NCBI View Article : Google Scholar
|
|
21
|
Dastan D, Soleymanekhtiari S and Ebadi A:
Peptidic compound as DNA binding agent: In silico fragment-based
design, machine learning, molecular modeling, synthesis, and DNA
binding Evaluation. Protein Pept Lett. 31:332–344. 2024.PubMed/NCBI View Article : Google Scholar
|
|
22
|
Gui C, Gao Y, Zhang R and Zhou G:
Bioinformatics analysis of lactylation-related biomarkers and
potential pathogenesis mechanisms in age-related macular
degeneration. Curr Genomics. 26:191–209. 2025.PubMed/NCBI View Article : Google Scholar
|
|
23
|
Gomase VS, Dhamane SP, Kemkar KR, Kakade
PG and Sakhare AD: Immunoproteomics: Approach to diagnostic and
vaccine development. Protein Pept Lett. 31:773–795. 2024.PubMed/NCBI View Article : Google Scholar
|
|
24
|
Mann J, Lyons M, O'Rourke J and Davies S:
Machine learning or traditional statistical methods for predictive
modelling in perioperative medicine: A narrative review. J Clin
Anesth. 102(111782)2025.PubMed/NCBI View Article : Google Scholar
|
|
25
|
Holler E, Ludema C, Ben Miled Z, Rosenberg
M, Kalbaugh C, Boustani M and Mohanty S: Development and validation
of a routine electronic health record-based delirium prediction
model for surgical patients without dementia: Retrospective
case-control study. JMIR Perioper Med. 8(e59422)2025.PubMed/NCBI View
Article : Google Scholar
|
|
26
|
Needleman L, Cronan JJ, Lilly MP, Merli
GJ, Adhikari S, Hertzberg BS, DeJong MR, Streiff MB and Meissner
MH: Ultrasound for lower extremity deep venous thrombosis:
Multidisciplinary recommendations from the society of radiologists
in ultrasound consensus conference. Circulation. 137:1505–1515.
2018.PubMed/NCBI View Article : Google Scholar
|
|
27
|
Tamariz L, Harkins T and Nair V: A
systematic review of validated methods for identifying venous
thromboembolism using administrative and claims data.
Pharmacoepidemiol Drug Saf. 21 (Suppl 1):S154–S162. 2012.PubMed/NCBI View
Article : Google Scholar
|
|
28
|
Drosdowsky A and Gough K: The charlson
comorbidity index: Problems with use in epidemiological research. J
Clin Epidemiol. 148:174–177. 2022.PubMed/NCBI View Article : Google Scholar
|
|
29
|
Liu C, Peng XX, Cai SY, Liu YL, Zhang C
and Hu F: Development of a pre-processing workflow for real world
data derived from multicenter clinical laboratories. Zhonghua Liu
Xing Bing Xue Za Zhi. 46:296–306. 2025.PubMed/NCBI View Article : Google Scholar : (In Chinese).
|
|
30
|
Namjoo-Moghadam A, Abedi V, Avula V,
Ashjazadeh N, Hooshmandi E, Abedinpour N, Rahimian Z,
Borhani-Haghighi A and Zand R: Machine learning-based cerebral
venous thrombosis diagnosis with clinical data. J Stroke
Cerebrovasc Dis. 33(107848)2024.PubMed/NCBI View Article : Google Scholar
|
|
31
|
Collins GS, Moons KGM, Dhiman P, Riley RD,
Beam AL, Van Calster B, Ghassemi M, Liu X, Reitsma JB, Van Smeden
M, et al: TRIPOD+AI statement: Updated guidance for reporting
clinical prediction models that use regression or machine learning
methods. BMJ. 385(e078378)2024.PubMed/NCBI View Article : Google Scholar
|
|
32
|
Collins GS, Reitsma JB, Altman DG and
Moons KG: Transparent reporting of a multivariable prediction model
for individual prognosis or diagnosis (TRIPOD): The TRIPOD
statement. BMJ. 350(g7594)2015.PubMed/NCBI View Article : Google Scholar
|
|
33
|
Hayssen H, Cires-Drouet R, Englum B,
Nguyen P, Sahoo S, Mayorga-Carlin M, Siddiqui T, Turner D, Yesha Y,
Sorkin JD and Lal BK: Systematic review of venous thromboembolism
risk categories derived from Caprini score. J Vasc Surg Venous
Lymphat Disord. 10:1401–1409.e7. 2022.PubMed/NCBI View Article : Google Scholar
|
|
34
|
Clinkenbeard K, Bossle K, Pape T,
Woltenberg LN and Saha S: Time to hip fracture surgery and
mortality. South Med J. 116:274–278. 2023.PubMed/NCBI View Article : Google Scholar
|
|
35
|
Ten Cate V, Prochaska JH, Schulz A, Nagler
M, Robles AP, Jurk K, Koeck T, Rapp S, Düber C, Münzel T, et al:
Clinical profile and outcome of isolated pulmonary embolism: A
systematic review and meta-analysis. EClinicalMedicine.
59(101973)2023.PubMed/NCBI View Article : Google Scholar
|
|
36
|
Ma R, Yu W, Tian J, Tang Y, Fang H, Ming X
and Liu H: Machine learning in the prediction of venous
thromboembolism: Systematic review and meta-analysis. J Med
Internet Res. 27(e77339)2025.PubMed/NCBI View
Article : Google Scholar
|
|
37
|
Selvin E, Steffes MW, Zhu H, Matsushita K,
Wagenknecht L, Pankow J, Coresh J and Brancati FL: Glycated
hemoglobin, diabetes, and cardiovascular risk in nondiabetic
adults. N Engl J Med. 362:800–811. 2010.PubMed/NCBI View Article : Google Scholar
|
|
38
|
Davis JW, Weller SC, Porterfield L, Chen L
and Wilkinson GS: Statin use and the risk of venous thromboembolism
in women taking hormone therapy. JAMA Netw Open.
6(e2348213)2023.PubMed/NCBI View Article : Google Scholar
|
|
39
|
Pencina KM, Thanassoulis G, Pencina MJ,
Toth PP and Sniderman AD: Hemoglobin A1c and abdominal obesity as
predictors of diabetes and ASCVD in individuals with prediabetes in
UK Biobank: A prospective observational study. Cardiovasc Diabetol.
23(448)2024.PubMed/NCBI View Article : Google Scholar
|
|
40
|
Jiao X, Zhang Q, Peng P and Shen Y: HbA1c
is a predictive factor of severe coronary stenosis and major
adverse cardiovascular events in patients with both type 2 diabetes
and coronary heart disease. Diabetol Metab Syndr.
15(50)2023.PubMed/NCBI View Article : Google Scholar
|
|
41
|
Merrell LA, Esper GW, Ganta A, Egol KA and
Konda SR: Impact of poorly controlled diabetes and glycosylated
hemoglobin values in geriatric hip fracture mortality risk
assessment. Cureus. 15(e36422)2023.PubMed/NCBI View Article : Google Scholar
|
|
42
|
Kanchanabat B, Stapanavatr W, Meknavin S,
Soorapanth C, Sumanasrethakul C and Kanchanasuttirak P: Systematic
review and meta-analysis on the rate of postoperative venous
thromboembolism in orthopaedic surgery in Asian patients without
thromboprophylaxis. Br J Surg. 98:1356–1364. 2011.PubMed/NCBI View Article : Google Scholar
|