Multidimensional instruments validated in Brazil for pain evaluation in the elderly: narrative review
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Multidimensional instruments for the evaluation of pain in the elderly allows to identify the conditions that involves pain considering the emotional, physical, psychological, social, and economic aspects. They are ancillary tools in the prognosis of diseases, in addition to allowing a better approach to pain considering their assessed dimensions. The objective of this study was to review the literature on multidimensional instruments validated in Brazil for the evaluation of pain in the elderly, identifying instruments aimed at elderly people with neurocognitive disorders. CONTENTS: This was a narrative review of the literature with scientific articles searched in the electronic databases Pubmed, LILACS, and Scielo. The following keywords of the Portuguese language defined by the DeCS were used: pain, pain threshold, pain measurement, aging, and the elderly. Scientific articles on instruments validated in Brazil and published in the period from 2000 to 2018 were included. There were 38 articles, of which 33 were excluded, and only 5 articles were included. The validated instruments found for pain assessment in the elderly in Brazil were the McGill Pain Questionnaire, Geriatric Pain Measure, Pain Assessment Checklist for Seniors with Pain Assessment Checklist for Seniors with Limited Ability to Communicate, Non-Communicative Patient´s Pain Assessment Instrument, Pain Assessment in Advanced Dementia. CONCLUSION: The five instruments found offer the health professional a range of tools to understand pain better. Of these, three instruments allow the assessment of pain in the elderly with neurocognitive disorders.
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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 | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".