Instruments for the Assessment of Pain in Older Persons with Cognitive Impairment
Bibliographic record
Abstract
Pain in older persons with cognitive impairment is often unrecognized and inadequately treated. A major problem associated with this undertreatment is the challenging nature of pain assessment and in particular the selection of accurate and useful assessment instruments. The purpose of this study was to review pain measurement instruments for acute and chronic pain suggested for use with cognitively impaired older persons and to summarize available evidence on their reliability and validity. A systematic search for pain instruments was conducted using several bibliographic databases, supplemented by a manual search of the bibliographies of retrieved articles and review chapters and by articles received from experts and clinicians in the field. Instruments were retained for review when the pain instrument was used or recommended for use with older persons with cognitive impairment. Thirty-nine instruments were reviewed; nine were excluded for various reasons. Of the remaining 30, 18 were self-report and 12 were staff administered. There were no instruments for which all major tests of reliability or validity were reported. Reliability and validity data were basic or unavailable for many instruments. One instrument had excellent validity but no reliability data. The remaining instruments had weak or adequate reliability and validity. The authors conclude that there is a need for further rigorous development and testing of pain instruments for use with cognitively impaired older persons. An adequate instrument would be one component of an effective program for assessment and management of pain in this population.
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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 | 0.014 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| 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) | 0.002 | 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".