Measuring Movement-Exacerbated Pain in Cognitively Impaired Frail Elders
Bibliographic record
Abstract
OBJECTIVE: Prior research examining the utility of nonverbal measures of pain in persons with cognitive impairments has focused on acute procedurally-induced phasic pain (i.e., venipuncture and needle injections). The goal of the current project was to examine the utility of both self-report and nonverbal measures of pain in frail elders experiencing exacerbations of chronic musculoskeletal pain. These were assumed to be more representative of the day-to-day pain experience of elderly patients. DESIGN: Participants were 58 frail elders, 29 of whom had been found to have significant cognitive impairments. All were filmed as they undertook a series of structured activities (e.g., walking and reclining), and pain was assessed using self-report. Trained coders identified the incidence of pain-related behaviors using the videotapes. The various pain measures (i.e., self-report and nonverbal indices) were compared across both patient groups and the several activities. RESULTS: Consistent with our hypotheses, more pain was identified (using both self-report and nonverbal measures) when patients engaged in more physically demanding activities. Facial reactions varied as a function of patient cognitive status, with those participants who were cognitively impaired more responsive. Of the various nonverbal indices that we examined, guarded behavior appeared to be especially sensitive. The various pain indices were only modestly correlated with one another. CONCLUSIONS: This study supports the validity of self-report and behavioral measures of pain in frail elders with and without cognitive impairments. Each of the measures used contributed different information to pain assessment, suggesting that investigations of pain in elders with cognitive impairments should employ varying types of pain assessment tools.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".