Building consensus on the utility of central and peripheral physiological markers for neuropsychiatric symptoms of dementia: a Delphi Study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.334 | 0.300 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.003 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".