Cognitive Impairment and Pain Discrepancies in ADRD: Evaluating Pain Responses to tDCS Intervention
Bibliographic record
Abstract
Abstract Assessing pain in individuals with Alzheimer’s disease and related dementias (ADRD) is challenging due to cognitive impairments that reduce the reliability of self-reported pain ratings, such as the Numerical Rating Scale (NRS). While caregiver-observed assessments, such as the Mobilization-Observation-Behavior-Intensity-Dementia-2 (MOBID-2), provide options, discrepancies between self-reported and observer-rated pain across cognitive impairment levels remain understudied. This study examines how cognitive function, stratified by the Montreal Cognitive Assessment (MoCA-30), influences pain reporting and response to transcranial direct current stimulation (tDCS), a nonpharmacological pain intervention. In this double-blind, randomized, sham-controlled trial, 40 older adults with ADRD and chronic pain (tDCS: n = 20, Sham: n = 20) received home-based tDCS (2mA, 20 minutes/session) or sham stimulation for five consecutive days. Pain intensity was assessed at baseline, post-intervention, and 1-, 2-, and 3-month follow-ups using NRS and MOBID-2. Participants were categorized into mild (18–25) and moderate (10–17) cognitive impairment groups. The sample included 29 females, 36 White participants, and 23 married individuals. Results showed that cognitive impairment significantly moderated discrepancies between NRS and MOBID-2 (F(4,35) = 3.364, p = .020, η² = .278). A significant Time × MoCA Severity interaction (p = .012) indicated that participants with lower MoCA scores demonstrated greater discrepancies in pain ratings. MOBID-2 consistently detected pain reduction following active tDCS, whereas NRS was less sensitive to these changes, particularly in those with greater cognitive impairment. These findings highlight the importance of cognitive-adaptive pain assessments and reinforce tDCS as a promising nonpharmacological intervention for pain management in ADRD.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".