The realm of neurosurgery is currently witnessing a surge in primary research, underscoring the importance of adopting evidence-based approaches. Scoping reviews, as a type of evidence synthesis, offer a broad perspective and have become increasingly vital for managing the ever-expanding body of research in swiftly evolving fields. Recent research has indicated a rising prevalence of scoping reviews in healthcare literature. In this context, the concept of a ‘review of scoping reviews’ has emerged as a means to offer a higher level synthesis of insights. However, the field of neurosurgery appears to lack a comprehensive integration of scoping reviews. Therefore, the objective of this scoping review is to identify and evaluate the extent of scoping reviews within neurosurgery, pinpointing research gaps and methodological issues to enhance evidence-based practices in this dynamic discipline.

The method framework of Arksey and O’Malley will be used to conduct the scoping review. A thorough literature search will be performed on Medline, Scopus and Web of Science to find eligible studies using the keywords related to neurosurgery, scoping review and its variants. Two reviewers will independently revise all of the full-text articles, extract data and evaluate the study extent. A narrative overview of the findings from included studies will be given.

This review will involve secondary analysis of published literature, and therefore ethics approval is not required. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews checklist will be used to guide translation of findings. Results will be disseminated through peer-reviewed journals and presented in conferences via abstract and presentation.

To evaluate whether nephrotic syndrome (NS) and further corticosteroid (CS) use increase the risk of osteoporosis in Asian population during the period January 2000–December 2010.

Nationwide population-based retrospective cohort study.

All healthcare facilities in Taiwan.

A total of 28 772 individuals were enrolled.

26 614 individuals with newly diagnosed NS between 2000 and 2010 were identified and included in out study. 26 614 individuals with no NS diagnosis prior to the index date were age matched as controls. Diagnosis of osteoporosis prior to the diagnosis of NS or the same index date was identified, age, sex and NS-associated comorbidities were adjusted.

To identify risk differences in developing osteoporosis among patients with a medical history of NS.

After adjusting for covariates, osteoporosis risk was found to be 3.279 times greater in the NS cohort than in the non-NS cohort, when measured over 11 years after NS diagnosis. Stratification revealed that age older than 18 years, congestive heart failure, hyperlipidaemia, chronic kidney disease, liver cirrhosis and NS-related disease including diabetes mellitus, hepatitis B infection, hepatitis C infection, lymphoma and hypothyroidism, increased the risk of osteoporosis in the NS cohort, compared with the non-NS cohort. Additionally, osteoporosis risk was significantly higher in NS patients with CS use (adjusted HR (aHR)=3.397). The risk of osteoporosis in NS patients was positively associated with risk of hip and vertebral fracture (aHR=2.130 and 2.268, respectively). A significant association exists between NS and subsequent risk for osteoporosis.

NS patients, particularly those treated with CS, should be evaluated for subsequent risk of osteoporosis.

To develop an interpretable deep learning model of lupus nephritis (LN) relapse prediction based on dynamic multivariable time-series data.

A single-centre, retrospective cohort study in China.

A Chinese central tertiary hospital.

The cohort study consisted of 1694 LN patients who had been registered in the Nanjing Glomerulonephritis Registry at the National Clinical Research Center of Kidney Diseases, Jinling Hospital from January 1985 to December 2010.

We developed a deep learning algorithm to predict LN relapse that consists of 59 features, including demographic, clinical, immunological, pathological and therapeutic characteristics that were collected for baseline analysis. A total of 32 227 data points were collected by the sliding window method and randomly divided into training (80%), validation (10%) and testing sets (10%). We developed a deep learning algorithm-based interpretable multivariable long short-term memory model for LN relapse risk prediction considering censored time-series data based on a cohort of 1694 LN patients. A mixture attention mechanism was deployed to capture variable interactions at different time points for estimating the temporal importance of the variables. Model performance was assessed according to C-index (concordance index).

The median follow-up time since remission was 4.1 (IQR, 1.7–6.7) years. The interpretable deep learning model based on dynamic multivariable time-series data achieved the best performance, with a C-index of 0.897, among models using only variables at the point of remission or time-variant variables. The importance of urinary protein, serum albumin and serum C3 showed time dependency in the model, that is, their contributions to the risk prediction increased over time.

Deep learning algorithms can effectively learn through time-series data to develop a predictive model for LN relapse. The model provides accurate predictions of LN relapse for different renal disease stages, which could be used in clinical practice to guide physicians on the management of LN patients.

Carcinosarcoma (CS) is a rare and biphasic malignancy characterised by a highly invasive biological nature and poor prognosis. This study explored the epidemiology, site-specific characteristics and survival outcome of CS.

We conducted a retrospective study in the Surveillance, Epidemiology and End Results (SEER) database (1975–2018) for primary CS.

SEER database includes publicly available information from regional and state cancer registries in the US centres. A total of 5042 CS patients were identified. We selected the top five anatomic CS (uterus, double adnexa, lung, bladder and breast) patients for further analysis.

Incidence was estimated by geographical region, age, sex, race, stage and primary site. Trends were calculated using joinpoint regression. The cancer-specific survival (CSS) rate and initial treatment were summarised.

Nearly 80% of CS occurred in the uterus and double adnexa, followed by lung, bladder and breast. The elderly and black population presented the highest age-adjusted rate of CS. The rates of distant metastasis in CS progressively increased from 1989 to 2018. Atlanta was the area with the highest incidence at 0.7 per 100 000. Pulmonary and bladder CS more frequently occurred in men and were diagnosed with regional stage. Distant metastasis was mostly found in ovary/fallopian tube CS. Radiotherapy was more commonly applied in uterine CS, while adnexa CS cases were more likely to receive chemotherapy. Multiple treatments were more used in breast CS. Pulmonary CS seemed to suffer worse CSS (median: 9.92 months), for which radiotherapy might not provide survival benefits (HR 0.60, 95% CI 0.42 to 0.86). Compared with the common histological types in each site, CS had the shortest survival.

CS has unique clinical features in each primary site. Substantial prognosis variances exist based on tumour locations. The aggressive course is the common feature in CS at all sites.