Evidence map›Paper›PMID 40757106›Full record

ArticleJournal of medicine and life2025

Retrospective analysis of biomechanical features in orthopedic spine disorders: a study on predictive factors for spinal abnormalities.

Abdulsalam Mohammed Aleid, Nouf Abdullah Alyabis, Abdulrahman Ahmed Almebki, Faisal Dhafer Alshehri, Abdulelah Mohammed Asiri, Yezeid Faisal Almohsen, Renad Fahad Almymoni, Ryan Khater Alanzi, Bayan Eid Alzahrani, Eid Khaled Algaman and 2 more

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Article in Journal of medicine and life, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

12 authors.

Abdulsalam Mohammed AleidDepartment of Surgery, Medical College, King Faisal University, Hofuf, Ahsa, Saudi Arabia.
Nouf Abdullah AlyabisCollege of Medicine, Alfaisal University, Riyadh, Saudi Arabia.
Abdulrahman Ahmed AlmebkiCollege of Medicine, King Saud University, Riyadh, Saudi Arabia.
Faisal Dhafer AlshehriCollege of Medicine, King Khalid University, Abha, Saudi Arabia.
Abdulelah Mohammed AsiriCollege of Medicine, King Khalid University, Abha, Saudi Arabia.
Yezeid Faisal AlmohsenImam Mohammad Ibn Saud Islamic University, College of Medicine, Riyadh, Saudi Arabia.
Renad Fahad AlmymoniUnaizah College of Medicine, Qassim University, Unaizah, Saudi Arabia.
Ryan Khater AlanziCollege of Medicine, Shaqra University, Shaqraa, Saudi Arabia.
Bayan Eid AlzahraniCollege of Applied Medical Sciences, King Saud bin Abdulaziz University for Health Sciences, Jeddah, Saudi Arabia.
Eid Khaled AlgamanCollege of Medicine, Qassim University, Buraydah, Saudi Arabia.
Loai Saleh AlbinsaadDepartment of Surgery, Medical College, King Faisal University, Hofuf, Ahsa, Saudi Arabia.
Saud Nayef Salem AldanyowiDepartment of Surgery, Medical College, King Faisal University, Hofuf, Ahsa, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Abnormal spine biomechanics are associated with various orthopedic disorders. Identifying key biomechanical factors predictive of spinal abnormalities can improve diagnosis and treatment. This study aimed to determine whether specific pelvic biomechanical parameters are significant predictors of spinal abnormalities. We hypothesized that patients with abnormal spine conditions exhibit distinct pelvic measurements compared to those with normal spine conditions. A retrospective analysis was conducted on 1,181 patient records from January to March 2024, focusing on pelvic incidence (PI), pelvic tilt (PT), lumbar lordosis (LL), sacral slope (SS), pelvic radius (PR), and spondylolisthesis. Data were collected from a centralized orthopedic patient database, which integrates de-identified records from the author's institution and affiliated facilities under the Ministry of Health. This ensured a standardized approach to data entry, with regular audits to maintain accuracy and reliability. Patients' spine conditions were classified as normal or abnormal based on imaging and clinical examinations. Descriptive statistics summarized the data, and comparative analyses were performed to differentiate between normal and abnormal groups. Decision trees and logistic regression were used to identify significant predictors of spinal abnormalities. Model validation was performed using ROC analysis and 10-fold cross-validation. Preliminary analysis found significant differences between normal and abnormal groups for various factors. Logistic regression identified pelvic incidence, lumbar lordosis, sacral slope, and pelvic radius as significant predictors (

Indexed as

Spinal DiseasesSpineAdolescentAdultAgedBiomechanical PhenomenaFemaleHumansMaleMiddle AgedPelvisRetrospective StudiesYoung Adultbiomechanicsdecision treeslogistic regressionorthopedicretrospectivespine

Identifiers

PMID40757106
PMCPMC12314845

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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.