74 Acetaminophen (paracetamol) deprescribing in long-term care: case series from Edmonton, Canada
Bibliographic record
Abstract
<h3>Objectives</h3> Acetaminophen (paracetamol) use is prevalent in long-term care (LTC) on the premise that it is a safe medication that reduces pain and discomfort. However, evidence is accumulating that acetaminophen does not improve pain or quality of life and is also associated with adverse events including cardiac, gastrointestinal, and renal. There is also minimal literature on deprescribing acetaminophen in frail older adults. <h3>Method</h3> Case series of four LTC residents deprescribed acetaminophen from January–December 2022 in Edmonton, Canada. <h3>Results</h3> The four residents ranged in age from 80 to 93, female (n=3); dementia (n=3); severely frail per the Clinical Frailty Scale (n=4); chronic pain conditions (n=2); and severe osteoarthritis (n=2). On admission to LTC, these residents were on 11–16 medications per day, which included regularly scheduled acetaminophen (n=4); hydromorphone (n=1); codeine (n=1); Gabapentin (n=2); and pregabalin (n=1). The administration of acetaminophen was 650 mg 4 times/day (n=2); 500 mg 3 times/day (n=1); and 325 mg 3 times/day (n=1). Acetaminophen was gradually reduced each week by first reducing the dosage per administration to 325 mg and then reducing the number of administrations per day until acetaminophen was fully discontinued for all the residents. There was no noted increase in pain by the residents, family members, and nursing staff, and none of the residents had an increase in acetaminophen (as needed) or other pain medication. <h3>Conclusions</h3> It appears possible to deprescribe acetaminophen in frail older adults. Further studies are needed to determine the prevalence and health care cost of acetaminophen in long-term care; the best approach to deprescribing acetaminophen; and the impact of deprescribing acetaminophen on pain, quality of life, and adverse events.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".