Rationale: False localizing sign means that the lesion, which is the cause of the symptom, is remote or distant from the anatomical site predicted by neurological examination. This concept contradicts the classical clinicoanatomical correlation paradigm underlying neurological examinations.
Patient Concerns: A 54-year-old man consulted for the right sciatica-like leg pain that had aggravated 1 year ago. Radiological examinations revealed degenerative spondylolisthesis with instability and right-sided recess stenosis at the L4-5 level. After initial improvement following 3 transforaminal epidural steroid injections with gabapentin and antidepressant medication, there was a recurrence of the symptoms a year later, along with wasting of the right leg for several months. Physical examination revealed difficulty in heel-walking and a weakness of extension of the right big toe; tendon reflexes were normal. Lumbar spine radiographs revealed no new findings. The initial course of treatment was repeated, but was ineffective.
Diagnoses: Further cervicothoracic spine evaluations revealed a right-sided intradural-extramedullary mass and myelopathy at the C1-2 level.
Interventions: The cervical mass was surgically resected and identified histopathologically as a schwannoma.
Outcomes: Immediately after surgery, sciatica-like pain and weakness of right leg were completely resolved.
Lessons: It is difficult to make an accurate diagnosis if there are symptoms caused by false localizing sign. Additionally, it is even more difficult to diagnose false localizing sign accurately when there is a co-existing lumbar lesion that can cause the similar symptoms.
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http://dx.doi.org/10.1097/MD.0000000000012215 | DOI Listing |
PLoS One
January 2025
Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huai'an, Jiangsu, China.
Accurate detection of fabric defects is crucial for quality control in the textile industry. However, the task of fabric defect detection remains highly challenging due to the complex textures and diverse defect patterns. To address the issues of inaccurate localization and false positives caused by complex textures and varying defect sizes, this paper proposes an improved YOLOv8-based fabric defect detection method.
View Article and Find Full Text PDFOral Radiol
January 2025
Department of Software Engineering, Faculty of Engineering, Muğla Sıtkı Koçman University, Muğla, 4800, Turkey.
Objectives: Pulp stones are ectopic calcifications located in pulp tissue. The aim of this study is to introduce a novel method for detecting pulp stones on panoramic radiography images using a deep learning-based two-stage pipeline architecture.
Materials And Methods: The first stage involved tooth localization with the YOLOv8 model, followed by pulp stone classification using ResNeXt.
Nat Commun
January 2025
J. Heyrovský Institute of Physical Chemistry, Czech Academy of Sciences, Prague, Czechia.
Single-molecule localization microscopy (SMLM) allows imaging beyond the diffraction limit. Detection of molecules is a crucial initial step in SMLM. False positive detections, which are not quantitatively controlled in current methods, are a source of artifacts that affect the entire SMLM analysis pipeline.
View Article and Find Full Text PDFEnviron Manage
January 2025
School of Public Policy and Urban Affairs, Northeastern University, Boston, MA, USA.
Riverine flooding is increasing in frequency and intensity, requiring river management agencies to consider new approaches to working with communities on flood mitigation planning. Communication and information sharing between agencies and communities is complex, and mistrust and misinformation arise quickly when communities perceive that they are excluded from planning. Subsequently, riverfront community members create narratives that can be examined as truth regimes-truths created and repeated that indicate how flooding and its causes are understood, represented, and discussed within their communities-to explain why flooding occurs in their area.
View Article and Find Full Text PDFSensors (Basel)
January 2025
College of Resources and Environment, Shanxi University of Finance and Economics, Taiyuan 030006, China.
The Loess Plateau in northwest China features fragmented terrain and is prone to landslides. However, the complex environment of the Loess Plateau, combined with the inherent limitations of convolutional neural networks (CNNs), often results in false positives and missed detection for deep learning models based on CNNs when identifying landslides from high-resolution remote sensing images. To deal with this challenge, our research introduced a CNN-transformer hybrid network.
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