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Introduction: Guidelines published in 2013 recommend early closed reduction for cervical spine dislocation. There are two types of closed reduction: manual reduction and traction. Manual reduction can be performed early. In addition, it can correct rotation and requires a short time for complete reduction. We perform manual reduction for cervical spine dislocation. This study aimed to evaluate early manual reduction's success rate and safety for cervical dislocation. We also examined the relationship between time to reduction and improvement in paralysis. Methods: This retrospective cohort study included 361 patients with cervical spine injuries treated at our hospital between July 2010 and December 2021. We assigned patients to the early group if the time from injury to reduction was 6 hours and to the late group if >6 hours. We performed awake manual reduction on the patients. Furthermore, we compared reduction's success rate and safety, including neurological outcomes. Results: Overall, 46 patients were included in the study: 31 and 15 in the early and late groups, respectively. The success rate of reduction was 93%, and no neurological complications from reduction were observed. The neurological outcomes and reduction success rates were significantly superior in the early group than in the late group. Conclusions: Neurological outcomes were significantly superior when reduction was performed within 6 hours than after 6 hours. Manual reduction can be performed early, safely, and easily. It is effective for cervical spine dislocation requiring early reduction for an excellent neurologic prognosis.

The misdiagnosis of headache disorders is a serious issue, and AI-based headache model diagnoses with external validation are scarce. We previously developed an artificial intelligence (AI)-based headache diagnosis model using a database of 4000 patients’ questionnaires in a headache-specializing clinic and herein performed external validation prospectively. The validation cohort of 59 headache patients was prospectively collected from August 2023 to February 2024 at our or collaborating multicenter institutions. The ground truth was specialists’ diagnoses based on the initial questionnaire and at least a one-month headache diary after the initial consultation. The diagnostic performance of the AI model was evaluated. The mean age was 42.55 ± 12.74 years, and 51/59 (86.67%) of the patients were female. No missing values were reported. Of the 59 patients, 56 (89.83%) had migraines or medication-overuse headaches, and 3 (5.08%) had tension-type headaches. No one had trigeminal autonomic cephalalgias or other headaches. The models’ overall accuracy and kappa for the ground truth were 94.92% and 0.65 (95%CI 0.21–1.00), respectively. The sensitivity, specificity, precision, and F values for migraines were 98.21%, 66.67%, 98.21%, and 98.21%, respectively. There was disagreement between the AI diagnosis and the ground truth by headache specialists in two patients. This is the first external validation of the AI headache diagnosis model. Further data collection and external validation are required to strengthen and improve its performance in real-world settings.