Reduction and prevention of agitation in persons with neurocognitive disorders: an international psychogeriatric association consensus algorithm
Bibliographic record
Abstract
OBJECTIVES: To develop an agitation reduction and prevention algorithm is intended to guide implementation of the definition of agitation developed by the International Psychogeriatric Association (IPA). DESIGN: Review of literature on treatment guidelines and recommended algorithms; algorithm development through reiterative integration of research information and expert opinion. SETTING: IPA Agitation Workgroup. PARTICIPANTS: IPA panel of international experts on agitation. INTERVENTION: Integration of available information into a comprehensive algorithm. MEASUREMENTS: None. RESULTS: The IPA Agitation Work Group recommends the Investigate, Plan, and Act (IPA) approach to agitation reduction and prevention. A thorough investigation of the behavior is followed by planning and acting with an emphasis on shared decision-making; the success of the plan is evaluated and adjusted as needed. The process is repeated until agitation is reduced to an acceptable level and prevention of recurrence is optimized. Psychosocial interventions are part of every plan and are continued throughout the process. Pharmacologic interventions are organized into panels of choices for nocturnal/circadian agitation; mild-moderate agitation or agitation with prominent mood features; moderate-severe agitation; and severe agitation with threatened harm to the patient or others. Therapeutic alternatives are presented for each panel. The occurrence of agitation in a variety of venues-home, nursing home, emergency department, hospice-and adjustments to the therapeutic approach are presented. CONCLUSIONS: The IPA definition of agitation is operationalized into an agitation management algorithm that emphasizes the integration of psychosocial and pharmacologic interventions, reiterative assessment of response to treatment, adjustment of therapeutic approaches to reflect the clinical situation, and shared decision-making.
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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 | 0.037 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.010 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".