Staged and Combined Approach for Resection of Giant Posterior Fossa and Temporal Bone Schwannoma

Fredrickson, Vance L; Mortimer, Vance; Rennert, Robert C.; Gurgel, Richard K.; Couldwell, William T.

doi:10.1016/j.wneu.2022.07.115

Cited by 1 publication

(1 citation statement)

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“…Its typical manifestations include the disappearance of facial folds and nasolabial grooves, smoothing of forehead wrinkles, and drooping of the mouth corners [5,6]. These symptoms affect the patient's appearance and confidence and can significantly impact daily activities, such as swallowing difficulties and dry eyes [7,8]. In primary care settings, distinguishing between idiopathic and other causes of peripheral facial paralysis in patients presenting with facial muscle weakness is crucial for treatment and prognosis, yet this task poses significant challenges for clinicians [9,10,11].…”

Section: Introductionmentioning

confidence: 99%

Development of a clinical diagnostic model for Bell's palsy in patients with facial muscle weakness

Li,

Chen,

Lai

et al. 2024

Biomol Biomed

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Early diagnosis of Bell's palsy is crucial for effective patient management in primary care settings. This study aimed to develop a simplified diagnostic tool to enhance the accuracy of identifying Bell's palsy among patients with facial muscle weakness. Data from 240 patients were analyzed using seven potential clinical evaluation indicators. Two diagnostic benchmarks were established: one based on clinical assessment and the other incorporating magnetic resonance imaging (MRI) findings. A multivariate logistic regression model was developed based on these benchmarks, resulting in the construction of a predictive tool evaluated through latent class models. Both models retained four key clinical indicators: absence of forehead wrinkles, accumulation of food and saliva inside the mouth on the affected side, presence of vesicular rash in the ear or pharynx, and lack of pain or symptoms associated with tick exposure, rash, or joint pain. The first model demonstrated excellent discriminative ability (area under the curve [AUC] = 0.96, 95% confidence interval [CI] 0.94 - 0.99) and calibration (P < 0.001), while the second model also showed good performance (AUC = 0.88, 95% CI 0.83 - 0.92) and calibration (P = 0.005). Bootstrap validation indicated no significant overfitting. The latent class defined by the first model significantly aligned with the clinical diagnosis group, while the second model showed lower consistency.

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