[Challenges in pain assessment and management among individuals with intellectual and developmental disabilities : German version].
Bibliographic record
Abstract
INTRODUCTION: Intellectual and developmental disabilities (IDD) include conditions associated with physical, learning, language, behavioural, and/or intellectual impairment. Pain is a common and debilitating secondary condition compromising functional abilities and quality of life. OBJECTIVES: This article addresses scientific and clinical challenges in pain assessment and management in individuals with severe IDD. METHODS: This Clinical Update aligns with the 2019 IASP Global Year Against Pain in the Vulnerable and selectively reviews recurring issues as well as the best available evidence and practice. RESULTS: The past decade of pain research has involved the development of standardized assessment tools appropriate for individuals with severe IDD; however, there is little empirical evidence that pain is being better assessed or managed clinically. There is limited evidence available to inform effective pain management practices; therefore, treatment approaches are largely empiric and highly variable. This is problematic because individuals with IDD are at risk of developing drug-related side effects, and treatment approaches effective for other populations may exacerbate pain in IDD populations. Scientifically, we are especially challenged by biases in self-reported and proxy-reported pain scores, identifying valid outcome measures for treatment trials, being able to adequately power studies due to small sample sizes, and our inability to easily explore the underlying pain mechanisms due to compromised ability to self-report. CONCLUSION: Despite the critical challenges, new developments in research and knowledge translation activities in pain and IDD continue to emerge, and there are ongoing international collaborations.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.012 |
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".