Study Protocol: Quantitative evaluation of The Agitation in Alzheimer’s Screener for Caregivers AASC™, a novel tool for improving recognition of agitation in Alzheimer’s dementia
Bibliographic record
Abstract
Abstract Background Agitation, manifesting as aggressive and non‐aggressive behaviors, is one of the most common neuropsychiatric symptoms in Alzheimer’s dementia, presenting in approximately half of all patients. Despite the high prevalence, recognition of agitation in Alzheimer’s dementia (AAD) remains a challenge that impacts timely diagnosis and treatment. The International Psychogeriatric Association (IPA) established a new standard definition of agitation in cognitive disorders, which provides guidance for advancing recognition and improving patient care. The Agitation in Alzheimer’s Screener for Caregivers (AASC™), an easy‐to‐use and pragmatic tool, was developed based on IPA criteria to support caregivers and healthcare professionals (HCPs) in recognizing AAD, thereby facilitating caregiver‐HCP discussions and supporting timely treatment planning. Method The AASC™ was developed and qualitatively evaluated through a rigorous, iterative process involving clinical experts, patients, and caregivers. For quantitative validation, this prospective, multisite, single‐visit observational study will calculate predictive metrics for the identification of agitation by comparing responses on the AASC™, completed by caregivers, to HCPs judgement based on implementation of the IPA criteria (Figure 1). Community‐dwelling patients must have a recorded diagnosis of Alzheimer’s dementia; caregivers must be aged 18‐85 years, provide patient care ≥10 hours/week, and attend the office visit. Data will be collected and analyzed in 2 parts: an interim analysis (n = 50 dyads) will determine the rate of agreement (yes/no), and a final analysis (n = 150 dyads) will determine sensitivity and specificity of the AASC™. Caregiver‐reported items include caregiver and patient demographics and AASC™ responses. HCPs will complete assessments for the presence of IPA‐defined agitation and the severity of Alzheimer’s dementia. Result Results will include: descriptive statistics, the percentage agreement and Cohen’s kappa coefficient characterizing the interrater reliability between the caregiver‐completed AASC™ and IPA‐based HCP decision (part 1), sensitivity, specificity, positive and negative predictive values of the AASC™ against the HCP decision, goodness of fit metrics (e.g., concordance), and receiver operating characteristic curves (part 2). Conclusion The AASC™ was developed following a rigorous process established to support its medical credibility and implementation in clinical practice. Results from this study will quantitatively validate the AASC™, leveraging caregiver observations to aid in facilitating earlier identification and treatment of AAD.
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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 | 0.131 | 0.124 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.036 | 0.010 |
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".