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Background: To determine the quality and reliability of DCR YouTube videos as patient education resources and identify any associated factors predictive of video quality., Methods: A YouTube search was conducted using the terms "Dacryocystorhinostomy, DCR, surgery" on 12th of January 2022, with the first 50 relevant videos selected for inclusion. For each video, the following was collected: video hyperlink, title, total views, months since the video was posted, video length, total likes/dislikes, authorship (i.e. surgeon, patient experience or media companies) and number of comments. The videos were graded independently by a resident, a registrar and an oculoplastic surgeon using three validated scoring systems: the Journal of the American Medical Association (JAMA), DISCERN, and Health on the Net (HON)., Results: The average number of video views was 22,992, with the mean length being 488.12 s and an average of 18 comments per video. The consensus JAMA, DISCERN and HON scores were 2.1 ± 0.6, 29.1 ± 8.8 and 2.7 ± 1.0, respectively. This indicated that the included videos were of a low quality, however, only DISCERN scores had good interobserver similarity. Videos posted by surgeons were superior to non-surgeons when considering mean JAMA and HON scores. No other factors were associated with the quality of educational content., Conclusion: The quality and reliability of DCR related content for patient education is relatively low. Based on this study's findings, patients should be encouraged to view videos created by surgeons or specialists in preference to other sources on YouTube., (© 2024. The Author(s).)

Objective: Epilepsy surgery is known to be underutilized. Machine learning-natural language processing (ML-NLP) may be able to assist with identifying patients suitable for referral for epilepsy surgery evaluation., Methods: Data were collected from two tertiary hospitals for patients seen in neurology outpatients for whom the diagnosis of "epilepsy" was mentioned. Individual case note review was undertaken to characterize the nature of the diagnoses discussed in these notes, and whether those with epilepsy fulfilled prespecified criteria for epilepsy surgery workup (namely focal drug refractory epilepsy without contraindications). ML-NLP algorithms were then developed using fivefold cross-validation on the first free-text clinic note for each patient to identify these criteria., Results: There were 457 notes included in the study, of which 250 patients had epilepsy. There were 37 (14.8%) individuals who fulfilled the prespecified criteria for epilepsy surgery referral without described contraindications, 32 (12.8%) of whom were not referred for epilepsy surgical evaluation in the given clinic visit. In the prediction of suitability for epilepsy surgery workup using the prespecified criteria, the tested models performed similarly. For example, the random forest model returned an area under the receiver operator characteristic curve of 0.97 (95% confidence interval 0.93-1.0) for this task, sensitivity of 1.0, and specificity of 0.93., Significance: This study has shown that there are patients in tertiary hospitals in South Australia who fulfill prespecified criteria for epilepsy surgery evaluation who may not have been referred for such evaluation. ML-NLP may assist with the identification of patients suitable for such referral., Plain Language Summary: Epilepsy surgery is a beneficial treatment for selected individuals with drug-resistant epilepsy. However, it is vastly underutilized. One reason for this underutilization is a lack of prompt referral of possible epilepsy surgery candidates to comprehensive epilepsy centers. Natural language processing, coupled with machine learning, may be able to identify possible epilepsy surgery candidates through the analysis of unstructured clinic notes. This study, conducted in two tertiary hospitals in South Australia, demonstrated that there are individuals who fulfill criteria for epilepsy surgery evaluation referral but have not yet been referred. Machine learning-natural language processing demonstrates promising results in assisting with the identification of such suitable candidates in Australia., (© 2024 The Authors. Epilepsia Open published by Wiley Periodicals LLC on behalf of International League Against Epilepsy.)

Objective: Decompressive craniectomy (DC) remains a controversial intervention for intracranial hypertension among patients with aneurysmal subarachnoid haemorrhage (aSAH)., Methods: We identified aSAH patients who underwent DC following microsurgical aneurysm repair from a prospectively maintained registry and compared their outcomes with a propensity-matched cohort who did not. Logistic regression was used to identify predictors of undergoing decompressive surgery and post-operative outcome. Outcomes of interest were inpatient mortality, unfavourable outcome, NIS-Subarachnoid Hemorrhage Outcome Measure and modified Rankin Score (mRS)., Results: A total of 246 patients with aSAH underwent clipping of the culprit aneurysm between 01/09/2011 and 20/07/2020. Of these, 46 underwent DC and were included in the final analysis. Unsurprisingly, DC patients had a greater chance of unfavourable outcome (p < 0.001) and higher median mRS (p < 0.001) at final follow-up. Despite this, almost two-thirds (64.1 %) of DC patients had a favourable outcome at this time-point. When compared with a propensity-matched cohort who did not, patients treated with DC fared worse at all endpoints. Multivariable logistic regression revealed that the presence of intracerebral haemorrhage and increased pre-operative mid-line shift were predictive of undergoing DC, and WFNS grade ≥ 4 and a delayed ischaemic neurological deficit requiring endovascular angioplasty were associated with an unfavourable outcome., Conclusions: Our data suggest that DC can be performed with acceptable rates of morbidity and mortality. Further research is required to determine the superiority, or otherwise, of DC compared with structured medical management of intracranial hypertension in this context, and to identify predictors of requiring decompressive surgery and patient outcome., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)