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    Evidence of Autoimmune Etiology in Children with Small Fiber Neuropathy

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    Variation in guideline-concordant use of bone modifying agents for prostate cancer

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    Pediatric cardiac arrest outcome prediction using data-driven machine learning of early quantitative electroencephalogram (qEEG) features.

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    AIMS: Hypoxic-ischemic brain injury drives poor outcomes after pediatric cardiac arrest, highlighting the need for early prognostication. This study evaluates whether machine learning models using a high-dimensional set of quantitative EEG (qEEG) features improve prediction of unfavorable neurologic outcome compared to a previously studied 7-feature model. We also assessed performance stability over time and the added value of clinical variables. METHODS: Single-center retrospective cohort study of children aged 3 months to 18 years who experienced cardiac arrest and received EEG monitoring within 24 hours post-arrest. Patients with pre-arrest Pediatric Cerebral Performance Category (PCPC) \u3e3 were excluded. Unfavorable outcome was defined as death or PCPC ≥4 at hospital discharge or 30 days post-arrest. We extracted 164 qEEG features and trained models using three established algorithms. Performance was evaluated using area under the ROC curve (AUROC). RESULTS: Seventy patients were included (median age 7.0 years, IQR 1.5-11.5); 53% had unfavorable outcomes. Models using 164 qEEG features outperformed the 7-feature model: LASSO [0.81 (95% CI: 0.69-0.91) vs 0.45 (0.31-0.58)] and Random Forest [0.80 (0.67-0.90) vs 0.65 (0.50-0.78)]. Adding clinical variables did not improve performance. AUROCs were stable across 6-hour epochs from 6 to 24 hours. Higher phase locking value, fractal exponent, and coherence were associated with better outcomes; higher delta power and suppression ratio variability were associated with worse outcomes. CONCLUSIONS: Data-driven models using 164 qEEG features accurately predicted neurologic outcomes after pediatric cardiac arrest, with stable performance over time. Future work includes external validation to assess generalizability

    DIFFUSE ESOPHAGEAL HYPERKERATOSIS: A CASE STUDY

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    Frontal Lobe Neurocysticercosis Presenting With Schizoaffective Symptoms in an Adolescent Immigrant.

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    Neurocysticercosis (NCC), caused by the larval stage o

    A Case of Cutaneous T-cell Lymphoma: The Importance of a Dermatology Consultation.

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    Mycosis fungoides (MF), the predominant variant of cutaneous T-cell lymphoma (CTCL), typically manifests as pruritic patches, plaques, and tumors, often mimicking cellulitis. Misdiagnosis of cellulitis has led to substantial avoidable healthcare costs, unnecessary hospitalizations, increased risk of nosocomial infections, and antibiotic resistance. We report a case of cellulitis misdiagnosis in a patient with a history of MF. A 68-year-old male referred from plastic surgery due to suspected worsening of left lower leg cellulitis despite recent antibiotic treatment presented to the hospital. While admitted, the patient underwent a computed tomography scan, which revealed increased diffuse subcutaneous soft tissue changes in the ankle and medial thigh regions with several focal areas suspicious for tumor involvement. The patient received intravenous antibiotics with a plan for surgical debridement and possible amputation per plastic surgery recommendation. Oncology and infectious disease were also consulted as part of the multidisciplinary approach, and a dermatology consultation was recommended, which included a punch biopsy. Results were consistent with an atypical dermal lymphoid infiltrate consistent with CTCL. The patient was discharged with a one-week course of cephalexin and restarted brentuximab per oncology. During follow-up, the patient showed improvement with less erythema and swelling. Delays in CTCL treatment can worsen prognosis. The ALT-70 tool aids in excluding cellulitis. This case underscores the necessity of dermatology consultation for accurate diagnosis, aiming to avert future misdiagnoses and inappropriate treatment of skin conditions resembling cellulitis, such as MF

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    Lehigh Valley Health Network: LVHN Scholarly Works
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