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Record W4406196204 · doi:10.1002/alz.091349

Building consensus on the utility of central and peripheral physiological markers for neuropsychiatric symptoms of dementia: a Delphi Study

2024· article· en· W4406196204 on OpenAlexaff
Ka Sing Paris Lai, Samira Choudhury, Sanjeev Kumar, Amer M. Burhan

Bibliographic record

VenueAlzheimer s & Dementia · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsCentre for Addiction and Mental HealthUniversity of TorontoOntario Shores Centre for Mental Health SciencesThe Scarborough Hospital
Fundersnot available
KeywordsDementiaPeripheralMedicineDelphiPsychologyDiseaseComputer scienceInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background Neuropsychiatric symptoms (NPS) of dementia are a heterogenous group of non‐cognitive symptoms and behaviors that occur in up to 90% of individuals with the condition. Characterizing NPS is a major issue and current methods are unreliable as they rely on subjective observations. Automatic identification of behaviors using central and peripheral physiological markers may be helpful to detect behaviors, allow for early intervention, and prevent critical incidents in patients with dementia. Methods This study will use a modified Delphi methodology to develop a guide for the use of central and peripheral markers that can be used for the detection, assessment, and monitoring of treatment response for persons with dementia exhibiting NPS in institutions. Experts including clinicians, educators, and researchers will be recruited internationally to participate as expert panellists. This study will review the current literature regarding the use of central and peripheral markers and NPS (e.g. wearable devices, mobile tracking devices, heart rate variability, skin conductance, and electroencephalography). A list of survey items will be developed and presented to the expert panellists for review and feedback. Survey items will also be refined accordingly based on panellist feedback. It is anticipated that there will be a total of 3 rounds of the modified Delphi process. Implications NPS can cause distress to patients and caregivers and increase the risk of injuries to the patients themselves and those in their vicinity. It is difficult for caregivers in care facilities or at home to continuously monitor the persons with dementia. If expert recommendations can be achieved about the use of markers for the detection, assessment, and monitoring of treatment response for NPS, clinicians will be able to better provide early intervention and deliver personalized treatment plans for persons with dementia exhibiting NPS.

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)0.334
metaresearch head score (Gemma)0.300
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.334
Threshold uncertainty score0.822

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3340.300
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0090.003
Science and technology studies0.0060.007
Scholarly communication0.0060.009
Open science0.0040.022
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0030.001

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 head0.100
GPT teacher head0.401
Teacher spread0.301 · 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.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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