Case presentation of Safe and Effective Use of Medical Cannabis in the Elderly
Bibliographic record
Abstract
Introduction: The therapeutic benefits of medical cannabis have been demonstrated for a number of chronic conditions impacting the elderly population, such as pain management, and as alternatives to antipsychotic and opioid interventions, as well as end of life treatments. However, this therapeutic intervention has not become part of routine care for seniors living in long-term care facilities because of reduced public acceptance and stigma. The aim of this paper was to present case studies outlining the effective use of medical cannabis to treat elderly patients with a variety of medical conditions and symptoms such as: post traumatic stress disorder, pain, anxiety, delusions, as well as palliative care. Cannabis was also used to taper antipsychotic medications, and for managing those in palliative care. Case Presentation: Three cases highlighting the use of medical cannabis are described, from the perspective of a nurse practitioner-led interdisciplinary team approach. Management and Outcome: Using a variety of combinations of medical cannabis (cannabidiol and delta-9-tetrahydrocannabinol) in oral formulations, the long-term care facility has achieved a dramatic reduction in the use of antipsychotic medications. Medical cannabis has shown alleviation of many symptoms such as: pain, dyspnea, agitation, fatigue, weakness, loss of appetite, nausea, vomiting, and twitching. Positive results were noted in several palliative care patients who received medical cannabis for pain and symptom management. Conclusion: As an adjunct therapy for managing post traumatic stress disorder and other conditions, medical cannabis has been effective in reducing symptoms and for improving the patients’ overall quality of life. Continued evaluation into the long effectiveness of medical cannabis provided to individuals over the age of 65 years is suggested. This nurse practitioner-led therapeutic intervention highlights the potential health benefits of medical cannabis and has clinical implications for practice and education.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".