Spine
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Observational Study
External Validation of SpineNet, an Open-source Deep Learning Model for Grading Lumbar Disc Degeneration MRI Features, Using the Northern Finland Birth Cohort 1966.
This is a retrospective observational study to externally validate a deep learning image classification model. ⋯ In this study, SpineNet has been benchmarked against expert human raters in the research setting. It has matched human reliability and demonstrates robust performance despite the multiple challenges facing model generalizability.
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Cross-sectional survey study. ⋯ Spine patients with limited health literacy have worse baseline PROM scores confounders and report worse general health. Further investigations are necessary to elucidate if limited health literacy is a marker or the root cause of these disparities. Findings from this study urge the consideration of patient health literacy when interpreting PROMs as well as the implications for patient assessment and discussion of treatment options.
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Retrospective cohort analysis. ⋯ The utility of IONM for elective ACDF remains uncertain; however, it continues to gain popularity for routine cases. For medical centers that lack similar resources to centers in more densely populated regions of NY state, reliable access to this technology is not a certainty. In our analysis of intraoperative neurological complications, it seems that IONM is not protective against neurological injury.
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This study used a French prospective national multi-center database of patients with spine metastasis (SpM). ⋯ Patients with poor ECOG-PS could benefit from surgery. Even though survival gain is small, it permits the preservation of their neurological function. By making ambulation possible, pain is decreased during the last months of their lives.
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A retrospective study. ⋯ 4.