Latent profile classification of cognitive function and its influencing factors in elderly patients with chronic pain after stroke

 Not solving stroke is the absolute stupidity out there!  You're all fired! Your comeuppance/screaming when you are the 1 in 4 per WHO that has a stroke  will be soul satisfying. 

Not solving a well-known problem is inexcusable!

• 39% to 55% post stroke pain (3 posts to October 2022)

Latent profile classification of cognitive function and its influencing factors in elderly patients with chronic pain after stroke

• 

  Jinglei Zhou

• L

  Lei Bo *

• Department of Neurology, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu, China

Abstract

Objectives: 

To identify the latent profile classification of cognitive function in elderly patients with chronic pain after stroke and explore the associated factors for patients in different categories.

Methods: 

Elderly patients with chronic pain after stroke hospitalized in the Department of Neurology of a hospital in Nanjing from August 2025 to January 2026 were selected as research subjects. Data were collected using a General Information Questionnaire, the Montreal Cognitive Assessment (MoCA), Hamilton Anxiety Scale (HAMA), Center for Epidemiological Studies Depression Scale (CES-D), Pain Catastrophizing Scale (PCS), and Elderly Social Participation Scale. Latent Profile Analysis (LPA) was used for patient classification, and multivariate logistic regression analysis was performed to identify predictive factors for different groups (P < 0.05).

Results: 

Patients’ cognitive function was classified into three latent profiles: High Cognitive Function-Low Abstraction Group, Moderate Cognitive Function-Low Orientation Group, and Low Cognitive Function Group. Logistic regression analysis showed that gender, pain type, pain intensity, anxiety, and depression were significant influencing factors of cognitive function across different categories (P < 0.05).

Conclusion: 

There is significant population heterogeneity in the cognitive function of elderly patients with chronic pain after stroke. Medical staff should implement precise interventions for patients in different categories to delay cognitive decline and improve their quality of life.

CT vs MRI: Assessing Stroke and Dementia Risk in White Matter Disease

 

My incompetent? doctor told me I had a bunch of white matter hyperintensities but never showed me them on any scan, so I don't know the size, location or any intervention needed, because my doctor knew nothing and did nothing. I have zero cognitive impairment and I'm 20 years out.

CT vs MRI: Assessing Stroke and Dementia Risk in White Matter Disease

 Patients with incidentally discovered white matter disease on computed tomography have a higher risk for future stroke or dementia than those with disease detected only on magnetic resonance imaging. White matter disease (WMD) detected on computed tomography (CT) identified patients at greater risk for future stroke or dementia than WMD detected only on magnetic resonance imaging (MRI), according to study results published in Neurology. Covert cerebrovascular disease (CCD), which includes covert brain infarction (CBI) and WMD, is frequently identified incidentally during neuroimaging in patients without a history of stroke or dementia. Although MRI is generally considered more sensitive for detecting these abnormalities, CT remains the most commonly used neuroimaging modality in routine clinical practice. Researchers therefore compared incidentally discovered CCD detected on CT and MRI to determine whether findings from each modality differed in their association with subsequent neurologic outcomes. Researchers conducted a retrospective cohort study of adults aged 50 years and older who underwent both head CT and brain MRI within a 30-day period between 2009 and 2022. They excluded patients with a prior history of stroke or dementia, as well as those with major neurologic symptoms suggestive of acute stroke. They used natural language processing to identify CBI and WMD from radiology reports and classify WMD severity. These findings highlight the importance of modality-specific interpretation of CCD in clinical practice and research. The analysis included 18,628 participants with a mean age of 64.9 years; 59.1% were women. The cohort was racially and ethnically diverse, with 41.4% identifying as non-Hispanic White, 32.7% as Hispanic, 12.0% as Asian or Pacific Islander, and 11.3% as African American. Cardiovascular risk factors were common, including hypertension (63.3%), hypercholesterolemia (71.3%), diabetes (30.3%), and a history of tobacco use (44.9%). The prevalence of CBI was similar across imaging modalities, occurring in 6.3% of CT scans and 6.1% of MRI scans. Overall agreement for CBI presence or absence was 91.6%. Among patients with CBI on MRI, 33.3% also had CBI on CT. Among patients with CBI on CT, 31.9% also had CBI on MRI. Researchers identified WMD in 60.5% of MRI reports compared with 24.4% of CT reports. Agreement for WMD presence was 57.6%. Among patients with WMD severity classified on both modalities, 47.9% received different severity classifications, and MRI assigned a higher severity grade than CT in 92.3% of those discordant cases. During a mean follow-up period of 4.4 years, 985 patients experienced stroke alone, 716 developed dementia alone, and 330 experienced both outcomes. Patients without WMD on either imaging modality had an incidence rate of stroke or dementia of 12.7 events per 1000 person-years. Rates increased to 22.6 among patients with WMD detected only on MRI, 37.0 among those with WMD detected only on CT, and 52.2 among those with WMD detected on both CT and MRI (all per 1000 person-years). After adjustment for demographic characteristics and vascular risk factors, patients with WMD detected only on MRI had a 23% higher risk for stroke or dementia than those without WMD on either modality (hazard ratio [HR], 1.23; 95% CI, 1.07-1.41). Patients with WMD detected only on CT had an even greater risk (HR, 1.46; 95% CI, 1.11-1.92), and those with WMD identified on both modalities had the greatest risk (HR, 1.82; 95% CI, 1.58-2.11). The researchers observed a similar pattern for CBI. Compared with patients without CBI on either modality, those with MRI-only CBI had a higher adjusted risk for stroke or dementia (HR, 1.47; 95% CI, 1.23-1.76), as did patients with CT-only CBI (HR, 1.26; 95% CI, 1.04-1.53) and those with CBI detected on both modalities (HR, 1.68; 95% CI, 1.33-2.11). The researchers suggested that MRI appears to detect a broader spectrum of white matter abnormalities, including milder disease that may not be visible on CT. In contrast, WMD identified on CT may represent more advanced or clinically significant cerebrovascular injury, potentially explaining its stronger association with future stroke or dementia. Study limitations include reliance on natural language processing of radiology reports rather than direct image review and the inclusion of only patients who underwent both CT and MRI. “These findings highlight the importance of modality-specific interpretation of CCD in clinical practice and research,” the study authors concluded.  Disclosures: This research was supported by the Alzheimer’s Drug Discovery Foundation and the National Institutes of Health. One study author declared affiliations with biotech, pharmaceutical, and/or device companies. Please see the original reference for a full list of disclosures.

Modified small vessel disease score as the top predictor of stroke outcome after thrombectomy: a CT-based machine learning study

 

Why are your predicting failure to recover RATHER THAN DELIVERING RECOVERY?

Laziness? Incompetence? Or just don't care? NO leadership? NO strategy? Not my job? Not my Problem!

You're all fired! You need to create EXACT RECOVERY PROTOCOLS! 

Prediction crapola like this does nothing to get survivors recovered! Your comeuppance when you have a stroke and don't recover will be a bitter pill for you to swallow.

Modified small vessel disease score as the top predictor of stroke outcome after thrombectomy: a CT-based machine learning study

• 

  Thiago Oscar Goulart 1† *

• 

  Rui Kleber do Vale Martins-Filho 2

• 

  Millene Rodrigues Camilo 2

• 

  Daniel Giansante Abud 2

• 

  Octávio Marques Pontes-Neto 2

• 1. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States

• 2. Department of Neuroscience and Behavioral Sciences, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, São Paulo, Brazil

Abstract

Background: 

Mechanical thrombectomy (MT) improves outcomes in ischemic stroke (IS) due to large vessel occlusion (LVO), but ~50% of patients fail to achieve functional independence.

Objectives: 

We investigated whether cerebral small vessel disease (cSVD), assessed by the modified Small Vessel Disease (mSVD) score and Brain Frailty Score (BFS), outperforms individual CT markers in predicting 90-day outcomes after MT.

Design: 

Prospective cohort with retrospective analysis.

Methods: 

We included 351 patients with anterior circulation LVO treated with MT. Admission CT was used to score cSVD markers (leukoaraiosis, atrophy, lacunes) and compute mSVD and BFS. Eight logistic regression models and a Random Forest algorithm were used to predict poor outcome [modified Rankin Scale (mRS) 3–6]. Model performance was evaluated using AUC-ROC and compared via DeLong tests.

Results: 

Poor outcomes were associated with older age, higher NIHSS, systolic blood pressure, glycemia, and more severe leukoaraiosis and atrophy. Severe mSVD (score = 3) independently predicted poor outcomes (OR = 3.267; CI: 1.731–6.168; p = 0.009). mSVD outperformed BFS and individual CT markers (AUC = 0.904 vs. 0.889/0.898; DeLong p < 0.05) and ranked as the top predictor in Random Forest (importance = 42.05). Treatment efficacy declined with increasing mSVD: the probability of a favorable outcome was 15.53% and poor outcome was 84.47% for mSVD = 3, compared to 89.23% and 10.77%, respectively, for mSVD = 0. A secondary model incorporating 24h NIHSS and hemorrhagic transformation improved discrimination (AUC = 0.954), but mSVD remained a key independent predictor.

Conclusions: 

In this prospective study in a middle-income country, mSVD score was the strongest predictor of post-thrombectomy outcome, outperforming BFS and isolated imaging markers. While cSVD does not contraindicate MT, it reflects reduced cerebrovascular resilience. Integrating mSVD into baseline CT evaluation may enhance risk stratification and treatment guidance.

More at link.

Passive sensing of gait and medication-related fluctuations in Parkinson’s disease

 Is your doctor competent enough to IMMEDIATELY get this for creating AN EXACT DAMAGE DIAGNOSIS to be followed by AN EXACT REHAB PROTOCOL FOR COMPLETE RECOVERY OF WALKING? Oh NO, your doctor is fucking incompetent; 

KNOWS NOTHING AND DOES NOTHING!

Passive sensing of gait and medication-related fluctuations in Parkinson’s disease

• Juyoung Jenna Yun,
• Charalambos Hadjipanayi,
• Arya Jahangiri,
• Alan Bannon,
• Timothy G. Constandinou &
• Shlomi Haar 

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Abstract

Background

Gait impairment is a hallmark symptom of Parkinson’s Disease (PD). Traditional clinical assessments cannot capture real-world motor fluctuations, as they are sparsely performed. We validated the use of nearables, passive sensing technologies, including Kinect RGB-D cameras and ultra-wideband (UWB) radar, for continuous, objective assessment of gait fluctuations in PD within a home-like setting.

Methods

Fifteen PD patients with mild symptoms and fourteen age- and sex-matched healthy controls (HC) performed 4-metre walking tasks in a living lab facility. Patients repeated the task during “ON” and “OFF” states of their daily medication cycle. Gait features, including stride length, stride time, and gait speed, were extracted from Kinect, radar, and a ground-truth smart floor. Data were analysed to assess inter-sensor agreements and group-level differences.

Results

Stride time demonstrated the highest agreement between devices (r = 0.903), while stride length was weaker (r = 0.779). Nevertheless, stride length from both Kinect and radar distinguished PD OFF from HC (camera q = 0.020; radar q = 0.005), and radar additionally differentiated ON from OFF (q = 0.020). Neither device differentiated PD ON from HC, indicating medication reduced observable gait differences.

Conclusions

Although some spatial metrics show device discrepancies, both systems demonstrate sensitivity to gait patterns and medication-dependent changes, supporting their use for longitudinal, real-world monitoring of motor symptoms.

How Chronic Stress Accelerates Aging (and How to Slow the Process) by Super Age

 

All stroke patients are under massive stress because your incompetent? doctor doesn't have 100% RECOVERY PROTOCOLS. Your doctor has known since medical school that stroke recovery is a complete shitshow and done nothing to fix that!

How Chronic Stress Accelerates Aging (and How to Slow the Process)

FDA-Listed Interactive Devices for Home Movement Rehabilitation After Stroke: A Mixed-Methods Study of Availability, User Needs, Information Gaps, and an Accompanying Dataset

 Does your competent? doctor know of all 57 of these and determined the best ones for your recovery? Oh NO, knows nothing and has done nothing! WOW! You picked a winner!

Here you are :

Key FDA Lists for Interactive Devices [1]

• AI-Enabled Medical Devices: The FDA's AI-Enabled Medical Devices List includes over 1,450 devices as of March 2026, mostly authorized via 510(k) clearance. These are heavily focused on radiology and cardiology.
• Sensor-Based Digital Health Technology: The Medical Devices that Incorporate Sensor-based Digital Health Technology list includes wearable, sensor-based devices that monitor physiological signals.
• AR/VR Medical Devices: The Augmented Reality and Virtual Reality in Medical Devices list tracks devices using these technologies for therapy and diagnostics.
• Breakthrough Devices Program: The Breakthrough Devices Program - FDA list features innovative devices that receive expedited development and review. [1, 2, 3, 4, 5, 6]

FDA-Listed Interactive Devices for Home Movement Rehabilitation After Stroke: A Mixed-Methods Study of Availability, User Needs, Information Gaps, and an Accompanying Dataset

by

Luis Garcia-Fernandez

1,*,

Juan C. Perez-Ibarra

1,

Andria J. Farrens

1,2,

Vicky Chan

1,

Joshua J. Macopson

1 and

David J. Reinkensmeyer

1

1

Mechanical and Aerospace Engineering Department, University of California Irvine, Irvine, CA 92617, USA

2

Department of Orthopaedics & Rehabilitation, University of New Mexico, Albuquerque, NM 87106, USA

*

Author to whom correspondence should be addressed.

Bioengineering 2026, 13(4), 387; https://doi.org/10.3390/bioengineering13040387

Submission received: 18 December 2025 / Revised: 6 March 2026 / Accepted: 24 March 2026 / Published: 27 March 2026

(This article belongs to the Special Issue Technological Advances in Neurorehabilitation)

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Versions Notes

Abstract

Technologies for home movement rehabilitation after stroke are rapidly expanding. However, for consumers, the number and nature of available products are unclear, and the information provided by device manufacturers varies widely. To understand this landscape, we conducted a mixed-methods, descriptive study in which we used the U.S. Food and Drug Administration (FDA) database to identify interactive devices for stroke rehabilitation suitable for home use. We then surveyed 13 individuals with stroke to determine what information they most wanted about home-based rehabilitation devices and contacted manufacturers to obtain those details. Thirteen FDA codes were associated with stroke rehabilitation devices, encompassing 57 devices produced by 40 companies. Nearly half were categorized under two codes: QKC (interactive rehabilitation exercise devices) and GZI (neuromuscular stimulators). Among devices for which information was available, 71% were listed after 2015, and 23% cost under $1000. The top information priorities for individuals with stroke were required usage to achieve therapeutic benefit, expected benefit, ease of use, and motivational features. Despite repeated outreach, only 45% of companies responded to our queries; among those that did, details were vague and variable. These results confirm that a large and growing number of FDA-listed devices are now available for home-based post-stroke motor rehabilitation. We further identify a need to establish industry standards for reporting ease of use, motivational effectiveness, and dose–response characteristics to help the intended consumers select appropriate technologies. The curated dataset generated in this study is provided as a resource for future work and may support the development of accurate Artificial Intelligence-based interfaces for identifying and comparing rehabilitation devices.

More at link.