Study Design: Prospective multicenter cohort study.

Objective: To assess the: (1) agreement between surgeon and independent review of fusion after single-level anterior cervical decompression and fusion, and (2) influence of surgeon impression of patient status on agreement.

Summary Of Background Data: Failure to achieve fusion can lead to poor functional outcome. Visual inspection of plain radiographs is used to assess fusion, but this assessment's reliability is not well understood.

Methods: Of 668 participants in the Cervical Spine Research Society Outcomes Study, 181 underwent single-level procedures. Three independent reviewers and each surgeon assessed fusion (i.e., radiographic trabecular bridging of the graft-vertebral body gap and absence of spinous process motion) on plain radiographs at 3 and 6 months after surgery. Agreement was evaluated with an intraclass correlation coefficient (ICC). The influence of surgeon impression of patient status on agreement was assessed with logistic regression analysis.

Results: Agreement was high among reviewers (ICC 0.822 to 0.892) but poor between reviewers and surgeons (ICC 0.308 to 0.484); disagreement was higher when the surgeon reported medical (odds ratio [OR] = 0.19, 95%; confidence interval [CI] 0.12, 0.30; P < 0.001), neurologic (OR = 0.13, 95% CI: 0.09, 0.21, P < 0.001), or functional (OR = 0.19, 95% CI: 0.12, 0.29, P < 0.001) improvement than when the surgeon did not report this improvement.

Conclusions: The finding that surgeons and independent reviewers disagreed on fusion assessment highlights the need for objective and reproducible measures of fusion.

Download full-text PDF

Source

Publication Analysis

Top Keywords

surgeons independent
8
fusion
8
fusion single-level
8
single-level anterior
8
anterior cervical
8
cervical spine
8
prospective multicenter
8
influence surgeon
8
surgeon impression
8
impression patient
8

Similar Publications

Identifying genetic differences between bipolar disorder and major depression through multiple genome-wide association analyses.

Br J Psychiatry

January 2025

Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles, USA; Department of Human Genetics, University of California Los Angeles, USA; and Department of Computational Medicine, University of California Los Angeles, USA.

Background: Accurate diagnosis of bipolar disorder (BPD) is difficult in clinical practice, with an average delay between symptom onset and diagnosis of about 7 years. A depressive episode often precedes the first manic episode, making it difficult to distinguish BPD from unipolar major depressive disorder (MDD).

Aims: We use genome-wide association analyses (GWAS) to identify differential genetic factors and to develop predictors based on polygenic risk scores (PRS) that may aid early differential diagnosis.

View Article and Find Full Text PDF

Objective: Artificial intelligence (AI) chatbots, including chat generative pretrained transformer (ChatGPT) and Google Gemini, have significantly increased access to medical information. However, in pediatric orthopaedics, no study has evaluated the accuracy of AI chatbots compared with evidence-based recommendations, including the American Academy of Orthopaedic Surgeons clinical practice guidelines (AAOS CPGs). The aims of this study were to compare responses by ChatGPT-4.

View Article and Find Full Text PDF

Supervised machine learning statistical models for visual outcome prediction in macular hole surgery: a single-surgeon, standardized surgery study.

Int J Retina Vitreous

January 2025

Department of Retina and Vitreous, Narayana Nethralaya, #121/C, 1st R Block, Chord Road, Rajaji Nagar, Bengaluru, 560010, India.

Purpose: To evaluate the predictive accuracy of various machine learning (ML) statistical models in forecasting postoperative visual acuity (VA) outcomes following macular hole (MH) surgery using preoperative optical coherence tomography (OCT) parameters.

Methods: This retrospective study included 158 eyes (151 patients) with full-thickness MHs treated between 2017 and 2023 by the same surgeon and using the same intraoperative surgical technique. Data from electronic medical records and OCT scans were extracted, with OCT-derived qualitative and quantitative MH characteristics recorded.

View Article and Find Full Text PDF

High-risk habitat radiomics model based on ultrasound images for predicting lateral neck lymph node metastasis in differentiated thyroid cancer.

BMC Med Imaging

January 2025

Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, No. 158 Shang tang Road, Hangzhou, Zhejiang, 310011, China.

Background: This study aims to evaluate the predictive usefulness of a habitat radiomics model based on ultrasound images for anticipating lateral neck lymph node metastasis (LLNM) in differentiated thyroid cancer (DTC), and for pinpointing high-risk habitat regions and significant radiomics traits.

Methods: A group of 214 patients diagnosed with differentiated thyroid carcinoma (DTC) between August 2021 and August 2023 were included, consisting of 107 patients with confirmed postoperative lateral lymph node metastasis (LLNM) and 107 patients without metastasis or lateral cervical lymph node involvement. An additional cohort of 43 patients was recruited to serve as an independent external testing group for this study.

View Article and Find Full Text PDF

Purpose: The development of the Diabetic Wound Assessment Learning Tool (DiWALT) has previously been described. However, an examination of its application to a larger, more heterogeneous group of participants is lacking. In order to allow for a more robust assessment of the psychometric properties of the DiWALT, we applied it to a broader group of participants.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!