Can Adding a Standardized Observational Tool to Interdisciplinary Evaluation Enhance the Detection of Pain in Older Adults with Cognitive Impairments?
Bibliographic record
Abstract
OBJECTIVE: The prevalence of chronic pain ranges from 40% to 80% in long-term care facilities, and it is especially high among older adults who are unable to communicate due to cognitive impairments. Although validated assessment tools exist, pain detection in this population is often done by interdisciplinary evaluation (IE), which largely relies on the subjective impression of health care providers. The aim of this study was to examine the agreement between the IE and validated observational pain tools. SETTING: We recruited 59 residents with limited ability to communicate. The pain behaviors of each participant were assessed with two validated tools, the Pain Assessment Checklist for Seniors with Limited Ability to Communicate (PACSLAC) and the Pain Assessment in Advanced Dementia (PAINAD), during transfer or mobilization. The results were then compared with the findings of the IE. RESULTS: The correlation between the PACSLAC and PAINAD was high (r = 0.79 [95% CI: 0.67-0.87]). However, we found a low to moderate association between the PACSLAC and the IE (r = 0.34 [95% CI: 0.09-0.55]), and a weaker association was observed between the PAINAD and the IE (r = 0.25 [95% CI: -0.02-0.48]). When the IE concluded that there was an absence of pain behavior, the PAINAD and the PACSLAC detected the presence of pain in 13.6% and 27.1% of the cases respectively. CONCLUSION: These results may be explained by an inability of IE to assess pain correctly or by instruments providing false positive results. Nevertheless, as detection of pain is difficult in this population, our research supports the use of validated tools to complement assessment of pain by the IE and make sure that no pain goes undetected.
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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 | 0.027 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".