Non-invasive stimulation techniques for fall prevention and balance control: an overview of reviews and meta-analyses

 What is your doctors' EXACT FALL PREVENTION PROTOCOL?  

Oh NO; doesn't have one; THAT'S PURE INCOMPETENCE! 

Dumping that on therapists is not allowed, the doctor is responsible for getting you 100% recovered. And s/he  COMPLETELY FAILED AT THAT, RIGHT? 

Non-invasive stimulation techniques for fall prevention and balance control: an overview of reviews and meta-analyses

• Zhihao Ren,
• Clemens Becker,
• Lorenzo Chiari &
• Pierpaolo Palumbo 

We’re sharing this article early to provide faster access to peer-reviewed, accepted research. It is citable and carries a permanent DOI. This version is subject to further edits and will be replaced automatically by the final Version of Record. All legal disclaimers apply.

Abstract

Background

Older adults and individuals with neurological disorders often experience balance impairments, which increase their risk of falling and compromise their functional independence. Numerous studies and systematic reviews have investigated the use of non-invasive stimulation techniques for fall prevention and balance control across various populations and device types.

Objective

To conduct an overview of systematic reviews assessing the efficacy of non-invasive stimulation techniques for fall prevention and balance control improvement in different target populations.

Methods

The review protocol was registered in the PROSPERO database (CRD420251023003). A systematic search was conducted for systematic reviews published up to March 2025. Reviews evaluating the efficacy of non-invasive stimulation techniques (i.e., non-invasive brain stimulation (NIBS), neuromuscular electrical stimulation (NMES), galvanic vestibular stimulation (GVS), and mechanical stimulation) on fall prevention or balance control in older adults or individuals with neurological disorders were included. The AMSTAR 2 tool was used to assess the methodological quality of the included reviews. Overall and subgroup meta-analyses were performed using random-effects models, with the standardized mean difference (SMD) as the effect size measure.

Results

We identified 21 systematic reviews. Three reviews (including one with a meta-analysis) were excluded from qualitative synthesis due to “low/critical low” quality. The remaining 18 reviews were included in the qualitative analysis, encompassing 328 original studies, which involved 11,692 participants. Of these, 13 reviews were included in the quantitative synthesis, of which 12 were pooled in the meta-analysis. The pooled results showed that non-invasive stimulation techniques had a moderate effect on improving balance control (SMD = 0.43, 95% CI, 0.34–0.52; = 8%, p = 0.366). Funnel plot inspection and Egger’s test (p = 0.267) indicated no evidence of publication bias. Subgroup analyses showed no statistically significant differences: by type of balance outcome (static vs. dynamic, p = 0.777), non-invasive stimulation techniques (p = 0.857), or target population (individuals with stroke or Parkinson’s disease, p = 0.750).

(Without even mentioning what the hell non-invasive interventions are; TOTALLY FUCKING USELESS! Don't your mentors and senior researchers have any clue how to do research?)

Conclusions

Non-invasive stimulation techniques may serve as a moderately effective(NOT GOOD ENOUGH!) therapeutic strategy for improving balance control, with potential functional benefits. The quality of evidence was moderate for NIBS but low for NMES and GVS. However, their clinical relevance should be interpreted with caution. The effectiveness in fall prevention remains to be established due to limited available evidence.

The pan-immune-inflammation value predicts stroke-associated pneumonia and poor outcome in spontaneous intracerebral hemorrhage: a machine learning approach

 You're that incompetent you don't know predictions DO NOTHING TOWARDS RECOVERY! Solve the correct problem!  You've known of the need to prevent pneumonia for years! I'd have you all fired for incompetence!

• 11% Stroke-associated pneumonia (21 posts to October 2020)

The pan-immune-inflammation value predicts stroke-associated pneumonia and poor outcome in spontaneous intracerebral hemorrhage: a machine learning approach

• 

  Anni Chen †

• J

  Jiahui Wang †

• Q

  Qiaoying Huang †

• 

  Chaoxiong Shen

• 

  Zhizhou Hu

• 

  Xiaohong Hu *

• Department of Neurology, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, Fujian, China

Abstract

Objective: 

Stroke-associated pneumonia (SAP) constitutes a major complication following spontaneous intracerebral hemorrhage (sICH), posing a significant clinical challenge for accurate prediction. This study aimed to evaluate whether integrating the pan-immune-inflammation value (PIV) enhances the predictive performance of machine learning (ML) models for SAP and poor functional outcome.

Methods: 

A retrospective cohort of 371 sICH patients was analyzed. Feature selection was performed using the least absolute shrinkage and selection operator (LASSO). Predictive models for SAP and poor outcome [modified Rankin Scale score (mRS) > 2 at 90 days] were developed and compared across nine ML algorithms. Model performance was assessed by discrimination (area under the receiver operating characteristic curve, AUC), calibration, and decision curve analysis (DCA). Interpretability was achieved via SHapley Additive exPlanations (SHAP).

Results: 

Elevated PIV was independently associated with SAP [odds ratio (OR) 13.55, 95% confidence interval (CI) 4.14–44.39; p < 0.0001] and poor 90-day functional outcome (OR 25.27, 95% CI 7.75–82.34; p < 0.0001). Among the algorithms, extreme Gradient Boosting (XGBoost) and logistic regression (LR) demonstrated the highest predictive performance for poor outcome (AUC 0.912) and SAP (AUC 0.856), respectively. Incorporating PIV significantly improved discriminatory ability (XGBoost: 0.926–0.942; LR: 0.905–0.931; both p < 0.05). The models demonstrated good calibration, provided net clinical benefit on DCA, and were interpretable through SHAP analysis, which consistently identified PIV as a critical predictor.

Interpretation: 

The integration of PIV into interpretable ML models significantly improves the accuracy of predicting SAP and functional outcome after sICH. This strategy, combining a systemic inflammatory biomarker with explainable ML, holds promise for advancing personalized risk stratification in neurocritical care.

Development and validation of a machine learning model based on multi-source clinical data for predicting the risk of early neurological deterioration in patients with ischemic stroke

 

Totally wrong objective; Survivors actually want early neurological deterioration prevented! Predictions DO NOTHING TOWARDS RECOVERY! 

What prevents early neurological deterioration is the needed research, not this crapola! You've known of the need for almost a decade but INCOMPETENTLY did this instead! You're fired!

• Early neurological deterioration (36 posts to January 2017)

Development and validation of a machine learning model based on multi-source clinical data for predicting the risk of early neurological deterioration in patients with ischemic stroke

• 

  Yue Li

• W

  Wei Wang

• Y

  Yilan Wei

• J

  Jing Han

• Y

  Yuan Shi

• Q

  Quping Ouyang *

• Neurological Disease Center, Beijing Shunyi District Hospital, Beijing, China

Abstract

Background and objective: 

Early neurological deterioration (END) is a critical clinical event associated with poor patient outcomes after acute ischemic stroke. Early identification of high-risk patients is crucial for timely clinical management. This study aimed to develop and validate a model for predicting END risk for acute ischemic stroke patients using machine learning algorithms.

Methods: 

This study retrospectively and consecutively enrolled 1,151 patients with acute ischemic stroke from the Stroke Center of Beijing Shunyi District Hospital between January 2021 and December 2024. END was defined as progressive worsening of neurological deficit symptoms after onset. Predictive variables were screened using univariate analysis and multiple feature selection methods (Treebag, Boruta, Bayesian). Nine machine learning algorithms (Decision Tree, Efficient Neural Network, K-Nearest Neighbors, Light Gradient Boosting Machine, Logistic Regression, Multilayer Perceptron, Random Forest, Simplified Support Vector Machine, Extreme Gradient Boosting) were employed to construct prediction models. Hyperparameters were optimized via 10-fold cross-validation, and model performance was evaluated in an internal validation cohort (30% of the sample). Primary evaluation metrics included the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, F1 score, and net benefit from decision curve analysis (DCA). The SHAP method was used to interpret the optimal model.

Results: 

A total of 161 patients (14.0%) developed END. Feature selection ultimately identified five key predictors: ischemic stroke etiological subtype, Oxford Community Stroke Project (OCSP) classification, age, atrial fibrillation history, and prior stroke history. In both the development and internal validation cohorts, the logistic regression model demonstrated favorable and stable performance (development cohort AUC: 0.787, 95% CI: 0.735–0.839; internal validation cohort AUC: 0.751, 95% CI: 0.668–0.834), with a low log-loss value. DCA suggested potential clinical utility of the logistic regression model. SHAP analysis revealed that the etiological subtype of ischemic stroke and age were the features contributing most to the model’s predictions.

Conclusion: 

This study successfully developed and validated a logistic regression model for predicting END risk. The model incorporates five routinely available clinical variables and demonstrates satisfactory predictive performance and interpretability. The developed online tool may assist clinicians in early risk stratification, providing a reference for personalized intervention.

Bright light therapy for post-stroke sleep disturbances: a systematic review and meta-analysis

 Let's check how long you've been brilliantly incompetent in not creating protocols on this!

• bright light therapy (9 posts to February 2017)

Bright light therapy for post-stroke sleep disturbances: a systematic review and meta-analysis

• 

  Xiaolan Tang

• 

  Qiuping Gu

• Y

  Yang Zhan

• X

  Xiaomei Sun

• F

  Fengqin Wang

• 

  Luchen Pan

• H

  Hua Zhao *

• Nursing Department, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China

Abstract

Objective: 

Sleep disturbances are common after stroke and may adversely affect neurological recovery. Although post-stroke sleep disturbances encompass insomnia symptoms, circadian rhythm sleep–wake disturbances, and sleep-disordered breathing, research has focused predominantly on obstructive sleep apnea. This review evaluated the effects of bright light therapy (BLT), a non-pharmacological intervention that may influence circadian regulation, on sleep outcomes after stroke.

Methods: 

Ten electronic databases covering English-and Chinese-language literature were searched from inception to April 9, 2026. Randomized controlled trials involving adults with stroke that evaluated validated subjective or objective sleep outcomes were eligible. Data were pooled using random-effects models when studies were sufficiently comparable clinically and methodologically. Risk of bias was assessed using the Cochrane Risk of Bias 2 tool. The primary outcomes were wake after sleep onset (WASO) and subjective sleep quality. The protocol was registered in PROSPERO (CRD420251111617).

Results: 

Five randomized controlled trials involving 343 participants were included. Three trials involving 181 participants contributed to the WASO analysis; the pooled estimate was not statistically significant (MD = −39.17 min, 95% CI −115.34 to 36.99; P = 0.31), and heterogeneity was substantial (I2 = 96%). Four trials involving 300 participants contributed to the subjective sleep quality analysis; the pooled estimate favored BLT (SMD = −0.85, 95% CI −1.38 to −0.32; P = 0.002), although heterogeneity was substantial (I2 = 78%). After exclusion of Song Chang-yu, the pooled effect remained statistically significant but was attenuated, and heterogeneity decreased to 0% (SMD = −0.55, 95% CI −0.83 to −0.28).

Conclusion: 

BLT may improve subjective sleep quality after stroke; however, evidence for objective sleep continuity remains uncertain because of the small evidence base and substantial heterogeneity. Larger, methodologically rigorous trials using standardized light parameters, standardized outcome measures, and direct circadian assessments are needed to clarify the clinical effects of BLT and identify optimal treatment protocols.

Systematic Review 

Registration:

https://www.crd.york.ac.uk/PROSPERO/view/CRD420251111617, identifier CRD420251111617.

Inflammatory burden index is associated with poor functional outcome and stroke-associated pneumonia in patients with primary brainstem hemorrhage: a retrospective cohort study

 Predicting failure to recover is grounds for firing! PURE INCOMPETENCE DISPLAYED!

Competent researchers would create pneumonia prevention protocols!

You've known of the problem for years and DONE NOTHING; YOU'RE FIRED!

• 11% Stroke-associated pneumonia (18 posts to October 2020)

Stroke research should deliver recovery protocols. You'll be screaming for them when you are the 1 in 4 per WHO that has a stroke!  

Inflammatory burden index is associated with poor functional outcome and stroke-associated pneumonia in patients with primary brainstem hemorrhage: a retrospective cohort study

• 

  Fangjie Luo 1† *

• J

  Jialiang Luo 2†

• X

  Xiaoyun Li 1

• 1. Department of Neurosurgery, The Affiliated Dongguan Songshanhu Central Hospital of Guangdong Medical University, Dongguan, Guangdong, China

• 2. Sun Yat-sen University, Dongguan, Guangdong, China

Abstract

Background: 

Primary brainstem hemorrhage (PBH) is a severe, poorly-prognostic stroke subtype. Since single biomarkers cannot reflect global inflammatory status, we constructed a composite Inflammatory Burden Index (IBI) incorporating neutrophil-to-lymphocyte ratio (NLR), C-reactive protein-to-albumin ratio (CAR), and systemic immune-inflammation index (SII), and assessed its links with 90-day poor functional outcome and stroke-associated pneumonia (SAP) in PBH patients.

Methods: 

This retrospective cohort included 347 PBH patients (2015–2023). The IBI was calculated as 0.50 × NLRz + 0.30 × CARz + 0.20 × SIIz, where the weights were assigned a priori from a qualitative synthesis of prior literature and expert judgment rather than derived from the study data. Patients were stratified into tertiles. Primary outcome was poor 90-day functional outcome (modified Rankin Scale 3–6). Secondary outcomes were 90-day mortality and SAP. Multivariable logistic regression, restricted cubic spline analysis, and decision curve analysis were performed. Incremental value over a reference clinical model (age, Glasgow Coma Scale, hematoma volume, intraventricular hemorrhage) was assessed via DeLong test, net reclassification improvement (NRI), and integrated discrimination improvement (IDI).

Results: 

The overall rates of poor outcome, mortality, and SAP were 30.3, 12.1, and 16.7%, respectively. Higher IBI tertiles were associated with progressively higher rates of poor functional outcome (18.1, 27.8, and 44.8%; p < 0.001), with graded but non-significant trends for SAP and mortality. In multivariable analysis, IBI independently predicted poor outcome (OR 2.31, 95% CI 1.64–3.26, p < 0.001) and SAP (OR 1.79, 95% CI 1.23–2.59, p = 0.002). RCS confirmed a monotonic dose–response relationship for poor outcome. IBI had the best AUC for poor outcome (0.663), outperforming SII and CAR, though not significantly better than NLR. Adding IBI to the clinical model improved AUC from 0.623 to 0.701 (DeLong p = 0.008; NRI 0.31, IDI 0.045). For SAP, NLR alone was marginally superior (AUC 0.615 vs. 0.614). Decision curve analysis showed net clinical benefit for the IBI model.

Conclusion: 

IBI independently predicts poor functional outcome and adds incremental value over clinical factors and most individual markers. However, its utility for SAP does not exceed that of NLR alone; hence IBI should be used selectively for functional prognostication, while NLR remains the preferred biomarker for SAP risk assessment, pending further external validation.

Graphical Abstract