Agitation – a case study highlighting the importance of the IPA criteria
Bibliographic record
Abstract
Agitation in Alzheimer disease and related dementias (ADRD) is associated with poorer function, loss of independence, risk of hospitalization and/or transfer to higher levels of care, use of pharmacotherapy, accelerated progression to severe dementia and death, higher health care costs, poorer quality of life, and substantial caregiver burden. Despite presenting in up to 60% of persons with AD, agitation-related behavioural changes are not detected reliably, and often not early enough. This poor and inconsistent detection of agitation has been based, historically, on a symptomatic view of agitation. The literature describes myriad symptoms as agitation, without consistently used standards, providing challenges to meaningful measurement, and consequently meaningful assessment of treatment response. To address this foundational issue in clinical care and research, the International Psychogeriatric Association (IPA) developed and recently validated criteria for Agitation in Cognitive Disorders. The IPA definition describes distressing behaviours in the domains of verbal aggression, physical aggression, and excessive motor activity. This syndromic approach to agitation is an improvement over previous symptomatic descriptors of agitation and helps standardize communication about these dementia-related behaviours. We present a clinical case incorporating the IPA agitation criteria to demonstrate the clinical benefit of using IPA nosology and nomenclature in assessing and measuring agitation, discussing these behaviours with family and caregivers, and making clinical decisions.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".