Purpose
This study was conducted to identify risk factors predicting the loss of cervical lordosis (LCL) in patients with multilevel ossification of the posterior longitudinal ligament (OPLL) following laminoplasty. Material and Methods: We conducted a retrospective analysis of data from patients who underwent laminoplasty at Chonnam National University Hospital between January 2013 and December 2022. Various radiological parameters and clinical outcome measures were collected perioperatively. Patients were divided into 2 groups according to the severity of LCL. We examined preoperative radiological parameters associated with LCL.
Results
We analyzed data from 109 patients (92 men and 17 women; mean age, 60.31±10.80 years). A higher T1 slope (odds ratio [OR], 1.420; p<0.001) and a lower extension ratio (OR, 0.883; p=0.019) were associated with a higher risk of LCL. T1 slope was shown to be an excellent predictor of LCL, with a cut-off value of 28° (p<0.001, area under the curve=0.918). Also, The T1 slope and extension ratio were statistically significant correlated with clinical outcomes.
Conclusions
T1 slope and extension ratio were significantly associated with LCL in patients with multilevel OPLL following laminoplasty. The cut-off value for the T1 slope was 28°, and the cut-off value for the extension ratio was 33. Therefore, in multilevel OPLL patients with a T1 slope exceeding 28° or an extension ratio below 33, a warning regarding the potential LCL should be given before performing cervical laminoplasty.

Objective
To investigate the utility of a deep learning model in diagnosing traumatic lumbar fractures on computed tomography (CT) images.
Summary of Background
Data: CT scans are widely used as the first choice for detecting spinal fractures in patients with severe trauma. Although CT scans have high diagnostic accuracy, fractures can occasionally be missed. Recently, deep learning has been applied in various fields of medical imaging.
Methods
CT images from 480 patients (3695 vertebrae) who visited a level-one trauma center with lumbar fractures were retrospectively analyzed. The diagnostic results were confirmed by two experienced musculoskeletal radiologists and one experienced spine surgeon using magnetic resonance imaging (MRI). Deep learning networks were employed for diagnosis, with 425 cases used for training and 55 cases for testing. Sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (AUROC) were calculated to evaluate diagnostic performance.
Results
The model successfully identified 107 out of 129 vertebrae with fractures, achieving a sensitivity of 82.95%, a specificity of 93.24%, an AUROC of 0.936, and an overall accuracy of 88.45%.
Conclusions
This study demonstrated that the deep learning model showed high accuracy in diagnosing traumatic lumbar fractures. This approach has the potential to assist spine specialists, radiologists, and trauma care experts. Further validation is needed to determine its effectiveness in clinical settings.

Objective
Postoperative urinary retention (POUR) is a common complication following lumbar spine surgery, significantly affecting functional recovery and Enhanced Recovery After Surgery (ERAS) protocols. POUR can lead to bladder overdistension, infections, prolonged hospital stays, and long-term detrusor dysfunction. Postoperative delirium (POD) can impair cognitive function and mobility, potentially triggering or exacerbating POUR. This study aims to investigate whether POD serves as an independent risk factor for POUR and to analyze other contributing factors to provide clinical management strategies.
Materials and Methods
A retrospective cohort study was conducted involving 420 patients who underwent lumbar spine surgery at a single tertiary medical institution between March 2021 and February 2024. POUR was defined as a post-void residual (PVR) bladder volume ≥300 mL measured via bladder ultrasound or requiring catheter reinsertion due to urinary retention. POD was diagnosed within 72 hours postoperatively using the Confusion Assessment Method (CAM) and was classified into three subtypes: hyperactive, hypoactive, and mixed. Multivariate logistic regression analysis was employed to identify the relationship between POD and POUR, with sensitivity and specificity assessed through Receiver Operating Characteristic (ROC) curve analysis.
Results
Among 420 lumbar spine surgery patients, 44 (10.5%) experienced POD. Of these, 16 (36.4%) were classified as hyperactive, 20 (45.5%) as hypoactive, and 8 (18.2%) as mixed type. POUR occurred in 28 of the POD patients (63.6%) compared to 71 of 376 patients without POD (18.9%), demonstrating a statistically significant difference (p<0.001). The analysis of POUR incidence by POD subtype revealed rates of 62.5% (10/16) for hyperactive POD, 60.0% (12/20) for hypoactive POD, and 75.0% (6/8) for mixed POD. Patients with mixed POD showed the highest POUR incidence, with a significant difference compared to hyperactive and hypoactive POD (p<0.05). Multivariate logistic regression analysis identified POD as an independent risk factor for POUR, increasing the likelihood by approximately 3.7 times (Odds Ratio, OR: 3.71; 95% Confidence Interval, CI: 1.95–7.06; p<0.001). Among POD subtypes, mixed POD presented the strongest association with POUR, increasing the risk by 4.8 times (OR: 4.84; 95% CI: 2.10–11.15; p<0.001). Hyperactive and hypoactive POD were also significant risk factors, increasing POUR risk by 3.0 times (OR: 3.04; 95% CI: 1.45–6.35; p=0.003) and 3.5 times (OR: 3.48; 95% CI: 1.69–7.19; p=0.001), respectively.
Conclusions
This study confirms that postoperative delirium (POD) is an independent risk factor for postoperative urinary retention (POUR) in lumbar spine surgery. The occurrence and subtype of POD significantly influence POUR incidence, with mixed POD presenting the highest risk. These findings highlight the importance of early diagnosis and prevention of POD as a strategy to effectively reduce POUR. A multidisciplinary approach integrating POD and POUR management could optimize postoperative outcomes and improve patient recovery.

Object: This pilot study aimed to evaluate the effectiveness of cervical epidural block (CEB) in improving upper extremity muscle strength in individuals diagnosed with cervical disc herniation.
Materials and Methods
5 patients diagnosed with cervical disc herniation were included and underwent a single CEB treatment. Patients were monitored weekly for 2 weeks via an outpatient clinic. Hand grip strength of affected side and difference of hand grip strength between affected and unaffected side (DHGS) was recorded using dynamometers; before treatment, immediate after treatment, after 1week and 2weeks of treatment. Pain on neck and radiating pain to upper extremity (UE) were measured using the visual analogue scale (VAS) before treatment and 2weeks after treatment.
Results
The median age of the patients was 48 (37.0-78.0) years, and the affected disc levels were C5-6 (3 patients), C6-7 (1 patient), and C5-6-7 (1 patient) respectively. In terms of pain, VAS of neck decreased from 6.8 to 3.2, and VAS of UE decreased from 7.4 to 3.0. Both hand grip strength of affected side and DHGS showed improvement when comparing before and after treatment (immediate, 1 week and 2 weeks after treatment) (p<0.001). However, there was no difference between immediate, 1 week and 2 weeks after treatment. The result was same for adjusting age and sex as covariates (p<0.001).
Conclusions
These findings suggest that CEB has the potential in improving pain and UE muscle weakness associated with cervical disc herniation. Further large-scale studies are necessary to validate these preliminary outcomes and establish the long-term effectiveness and sustainability of CEB in managing cervical disc herniation.