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Record W4404726428 · doi:10.1093/ageing/afae253

Unveiling mild behavioural impairment in Parkinson’s disease: insights from a systematic review

2024· review· en· W4404726428 on OpenAlexaff
Bin Hu

Bibliographic record

VenueAge and Ageing · 2024
Typereview
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineParkinson's diseaseCognitive impairmentDiseaseSystematic reviewMEDLINEPsychiatryPhysical medicine and rehabilitationPathology

Abstract

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Editorial to accompany: Mild Behavioural Impairment in Parkinson’s Disease: A Systematic Review [ 6]. Non-motor symptoms, particularly behavioural changes, can be identified and categorized as Mild Behavioural Impairment (MBI). The reported prevalence of MBI in PD can range from 20% to 84.1%. The inconsistency in diagnostic criteria and assessment tools for MBI poses a significant challenge. Parkinson’s disease (PD) is globally recognised for its cardinal motor symptoms—tremor, rigidity and bradykinesia. However, an equally significant but often under-appreciated aspect of PD is its non-motor symptoms, particularly behavioural changes categorised as mild behavioural impairment (MBI). A recent systematic review by Yu et al. [6] titled ‘Mild Behavioural Impairment in Parkinson’s Disease: A Systematic Review’ sheds light on the prevalence, characteristics, and implications of MBI in individuals with PD (PwP). This editorial aims to highlight the key findings of this review, discuss current urgent issues in MBI research, and emphasise the necessity of integrating MBI recognition into clinical practice. Yu et al. [6] conducted a comprehensive analysis of nine studies from five distinct research institutions, focusing on the prevalence and characteristics of MBI in PwP. The review revealed considerable variability in the reported prevalence of MBI, ranging from 20% to 84.1%. This variation was primarily attributed to differences in diagnostic criteria and assessment tools used across studies. For instance, some studies utilized the mild behavioural impairment checklist (MBI-C) with varying cut-off scores, while others relied on the Neuropsychiatric Inventory Questionnaire or the International Society to Advance Alzheimer’s Research and Treatment–Alzheimer’s Association criteria. A significant finding from Yu et al. [6] is the association between MBI and impaired cognitive function in PwP. Patients with MBI generally exhibited diminished cognitive performance, as indicated by lower scores on the Mini–Mental State Examination or Montreal Cognitive Assessment. Importantly, no substantial differences were observed in age, disease duration, or motor symptom severity between PwP with and without MBI, suggesting that MBI could serve as an early indicator of cognitive decline independent of these factors. The review also highlighted that affective dysregulation and impulse dyscontrol are the most prevalent MBI subdomains in PwP. Yu et al. [6] found that AD and ID were primary contributors to MBI, whereas abnormal perception and social inappropriateness were less common. This pattern underscores the need for clinicians to pay particular attention to mood disturbances and impulse control issues in the early stages of PD. While the systematic review by Yu et al. [6] provides valuable insights, it also brings to the forefront several urgent issues in the field of MBI research that warrant attention, which are as follows: 1. Lack of Standardised Diagnostic Criteria. The inconsistency in diagnostic criteria and assessment tools for MBI poses a significant challenge. The variability in prevalence rates across studies underscores the urgent need for standardisation. Without uniform diagnostic protocols, comparing results and drawing definitive conclusions about MBI in PD becomes difficult. As noted by Ismail et al. [3], the development of standardised tools like the MBI-C is crucial for consistent assessment and research comparability. 2. Under-diagnosis in Clinical Practice. MBI remains under-diagnosed in clinical settings, often overshadowed by the focus on motor symptoms. The subtlety and variability of MBI symptoms lead to them being overlooked or misattributed to normal aging or stress-related changes [4]. This under-recognition can result in missed opportunities for early intervention, potentially allowing cognitive decline to progress unchecked. 3. Overlap with Medication Side Effects. An important issue is the challenge in distinguishing MBI symptoms from side effects of dopaminergic treatments. Dopamine agonists, commonly used in PD management, can exacerbate behavioural symptoms like impulse control disorders [5]. This overlap complicates the clinical picture and necessitates careful assessment to ensure appropriate treatment strategies. 4. Need for Longitudinal Studies. Yu et al. [6] highlight the paucity of longitudinal studies focusing on MBI in PD. The lack of long-term data limits our understanding of how MBI symptoms develop and progress over time, as well as their potential role as predictors for PD-related dementia or increased dependency. Addressing this gap is crucial for developing effective interventions and improving patient outcomes. The urgent need for longitudinal studies cannot be overstated. Researchers should prioritise studies that track the progression of MBI over time to better understand its relationship with cognitive decline and disease progression. Standardising diagnostic criteria and assessment methods will enhance the comparability of research findings. 5. Multidisciplinary Approach and Policy Implications. Implementing a team-based approach involving neurologists, psychiatrists, psychologists and other specialists can enhance patient care by addressing the complex interplay of motor and non-motor symptoms [1]. Additionally, advocating for policies that support research funding and resource allocation for MBI can facilitate advancements in this field. The findings of Yu et al. [6] underscore the critical need for early detection of MBI in PwP. Identifying behavioural symptoms early provides an opportunity for timely interventions that may slow cognitive decline and improve quality of life. Clinicians should be vigilant in monitoring for signs of AD and ID, given their prominence in MBI among PwP. Incorporating comprehensive tools like the MBI-C into routine clinical evaluations can enhance the detection of MBI. Standardising the use of these tools will improve the consistency of diagnoses and facilitate better patient management. A personalised approach to treatment that considers the individual’s neuropsychiatric profile and disease stage is essential. While pharmacological interventions may be beneficial for certain symptoms, non-pharmacological strategies such as cognitive-behavioural therapy can also play a vital role [2]. Tailoring interventions can address the specific needs of each patient, potentially improving adherence to treatment and overall outcomes. There is a pressing need for healthcare systems and policymakers to support initiatives aimed at improving MBI recognition and management. Allocating resources for clinician training, patient education, and research funding is essential to advance this field. In summary, the systematic review by Yu et al. [6] provides valuable insights into the prevalence and characteristics of MBI in Parkinson’s disease. Recognising MBI as a significant component of PD is crucial for improving patient outcomes. Early identification and management of behavioural symptoms can enhance quality of life, reduce caregiver burden, and potentially slow cognitive decline. By integrating MBI assessments into clinical practice and addressing the urgent issues in research, we can move towards a more holistic and effective approach to Parkinson’s disease management. None. None.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0090.007
Bibliometrics0.0060.007
Science and technology studies0.0000.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.045
GPT teacher head0.320
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2024
Admission routes1
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