Older Adults with Alzheimer’s Disease, Comorbid Arthritis and Prescription of Psychotropic Medications
Bibliographic record
Abstract
OBJECTIVES: It is assumed that analgesia is underutilized among those with Alzheimer disease and that these patients may be inappropriately prescribed neuroleptics and benzodiazepines. The current study examines this assertion. DESIGN: For this study, prescription levels of analgesics and psychotropic medications for Alzheimer disease patients with (n=245) and without (n=215) musculoskeletal conditions (i.e., arthritis or rheumatism) are compared. SETTING: A national sample of community dwelling and institutionalized older adults was identified from the Canadian Study of Health and Aging (CSHA). PARTICIPANTS: Persons from 36 cities and surrounding rural areas over 64 years of age were randomly identified for the CSHA from government health records in all but one province. MEASUREMENTS: Prescribed analgesic and psychotropic medications were examined, as well as dementia severity and dementia related behavioural disturbance. RESULTS: Less than half of Alzheimer patients with arthritis or rheumatism were treated for pain (ie, 109 of 245 patients); they were also more likely to be prescribed benzodiazepines compared with Alzheimer patients without musculoskeletal conditions (subsequent to initial consideration for analgesia, dementia severity and dementia-related behaviours; Dchi(2)[Ddf =1] =3.97, P=0.046). CONCLUSIONS: These findings are in accord with prior research attesting to the undertreatment of pain among older adults. These results can be generalized with greater confidence, given the random composition of the patient sample.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".