Audiovestibular Findings in Patients with Concurrent Superior Canal Dehiscence and Vestibular Schwannoma.

OBJECTIVE: To describe the clinical-instrumental findings in case of concurrent superior canal dehiscence (SCD) and ipsilateral vestibular schwannoma (VS), aiming to highlight the importance of an extensive instrumental assessment to achieve a correct diagnosis. STUDY DESIGN: Retrospective case review. SETTING: Tertiary referral center. PATIENTS: Five patients with concurrent SCD and VS. INTERVENTION: Clinical-instrumental assessment and imaging. MAIN OUTCOME MEASURE: Clinical presentation, audiovestibular findings, and imaging. RESULTS: The chief complaints were hearing loss (HL) and unsteadiness (80%). Other main symptoms included tinnitus (60%) and pressure-induced vertigo (40%). Mixed-HL was identified in three patients and pure sensorineural-HL in 1, including a roll-over curve in speech-audiometry in two cases. Vibration-induced nystagmus was elicited in all cases, whereas vestibular-evoked myogenic potentials showed reduced thresholds and enhanced amplitudes on the affected side in three patients. Ipsilesional weakness on caloric testing was detected in three patients and a bilateral hyporeflexia in one. A global canal impairment was detected by the video-head impulse test in one case, whereas the rest of the cohort exhibited a reduced function for the affected superior canal, together with ipsilateral posterior canal impairment in two cases. All patients performed both temporal bones HRCT scan and brain-MRI showing unilateral SCD and ipsilateral VS, respectively. All patients were submitted to a wait-and-scan approach, requiring VS removal only in one case. CONCLUSION: Simultaneous SCD and VS might result in subtle clinical presentation with puzzling lesion patterns. When unclear symptoms and signs occur, a complete audiovestibular assessment plays a key role to address imaging and diagnosis.

Tumor progression in tympanojugular paragangliomas: the role of radiotherapy and wait and scan.

INTRODUCTION: Tympanojugular paragangliomas (TJ PGLs) are rare tumors characterized by bone infiltration and erosion and a close relationship with critical structures, such as cranial nerves and internal carotid artery. For these reasons, their management represents a tough challenge. Since the fifties, radio-therapy (RT) has been proposed as an alternative treatment aimed at avoiding tumor progression. However, the indolent nature of the tumor, characterized by slow growth, is a crucial factor that needs to be considered before offering radiation. METHODS: This study aims to examine tumor progression in RT patients through a systematic review of the literature and in TJ PGL patients who underwent solely wait and scan at our department. RESULTS: The rate of tumor progression in the RT group was 8.9%, while in the wait and scan cohort was 12.9%. This data suggests the innate slow growth of PGLs. However, it is not possible to draw certain conclusions because of the wide heterogeneity of the studies. CONCLUSION: When complete surgical excision of TJ PGLs is not feasible, appropriate counseling and patient selection, including comprehensive tumor classification, should be performed before proposing RT to control tumor progression, since wait and scan may represent a reasonable option in selected cases.

The wounding potential of assault rifles: analysis of the dimensions of entrance and exit wounds and comparison with conventional handguns. A multicentric study.

The appearance of a gunshot wound (GSW) is greatly influenced by the velocity of the projectile, where high-velocity projectiles (HVPs) are defined as ballistic agents reaching a muzzle velocity of > 600 m/s fired from assault rifles. The aim of the study is to present and explain the differences in the dimensions of entrance and exit wounds between the most used handguns and assault rifles and to propose a predictor of HVP, i.e., the ratio of exit and entrance wounds (EX/ENR). The surface area of entrance and exit GSWs and the EX/ENR were calculated. 66 perforating GSWs produced by NATO FMJ 7.62 × 52 mm and 5.56 × 42 mm fired from assault rifles were assigned to the HVP, while 64 lesions produced by conventional projectiles fired from revolvers and semi-automatic pistols were assigned to the low-velocity projectile (LVP) group. The dimensions of the exit wounds of the HVP group were significantly higher when compared to the LVP group (95% CI 0.9886-2.423, p < 0.05). The HVP group showed significantly higher values for the EX/ENR when compared to the LVP group (95% CI 2.617-7.173, p < 0.05). The evaluation of the EX/ENR can be considered an adequate tool to assess the type of weapon involved and to roughly estimate the associated wounding mechanisms, which can guide both the physician in the management and treatment of the patients affected by GSW, and the forensic pathologist in crime investigation.

Monitoring the Epidemiology of Otitis Using Free-Text Pediatric Medical Notes: A Deep Learning Approach.

Free-text information represents a valuable resource for epidemiological surveillance. Its unstructured nature, however, presents significant challenges in the extraction of meaningful information. This study presents a deep learning model for classifying otitis using pediatric medical records. We analyzed the Pedianet database, which includes data from January 2004 to August 2017. The model categorizes narratives from clinical record diagnoses into six types: no otitis, non-media otitis, non-acute otitis media (OM), acute OM (AOM), AOM with perforation, and recurrent AOM. Utilizing deep learning architectures, including an ensemble model, this study addressed the challenges associated with the manual classification of extensive narrative data. The performance of the model was evaluated according to a gold standard classification made by three expert clinicians. The ensemble model achieved values of 97.03, 93.97, 96.59, and 95.48 for balanced precision, balanced recall, accuracy, and balanced F1 measure, respectively. These results underscore the efficacy of using automated systems for medical diagnoses, especially in pediatric care. Our findings demonstrate the potential of deep learning in interpreting complex medical records, enhancing epidemiological surveillance and research. This approach offers significant improvements in handling large-scale medical data, ensuring accuracy and minimizing human error. The methodology is adaptable to other medical contexts, promising a new horizon in healthcare analytics.