A Comparative Investigation of Observational Pain Assessment Tools for Older Adults With Dementia
Bibliographic record
Abstract
OBJECTIVES: Pain assessment in patients with dementia and severe limitations in ability to communicate can be challenging. The American Geriatrics Society (AGS) encourages the incorporation of 6 behavioral domains (ie, facial expressions, verbalizations/vocalizations, body movements, changes in interpersonal interactions, changes in activity patterns/routines and mental status changes) when conducting pain assessments among seniors with dementia. We investigated 6 observational pain assessment measures (differing with respect to the extent that they cover the AGS-recommended domains) under 2 different pain conditions (ie, influenza vaccination and movement-exacerbated pain) in a sample of long-term care residents with cognitive impairments. Given that a criticism of pain assessment scales is that many of the pain behaviors assessed are highly overlapping with symptoms of delirium, we also investigated the impact of the elimination of delirium-related items. RESULTS: Consistent with expectations, all measures were able to differentiate between pain and baseline conditions. Reliability and validity varied across measures. Most measures continued to differentiate between pain and baseline states after items that overlap with delirium were eliminated. DISCUSSION: Our results provide much needed psychometric information regarding newly developed pain assessments measures for seniors with dementia. The measures differed with respect to ability to differentiate pain-related from non-pain-related states. Moreover, our findings support the utility of comprehensive coverage of the AGS-recommended pain assessment domains.
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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 | 0.015 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".