Confabulation and Fluctuating Memory: A Case of Alzheimer’s Dementia in a Culturally Diverse Patient
Bibliographic record
Abstract
Abstract Background Confabulation, the fabrication of details with short lucid intervals, hinders the diagnosis of Alzheimer’s and other dementias. This case report explores the complexities of diagnosing and managing Alzheimer’s dementia in a culturally diverse patient exhibiting fluctuating memory and confabulation, emphasizing challenges heightened by cultural and linguistic factors. Case An 84‐year‐old African descent man resided with his family in the United States, independent in his basic daily activities but struggling with instrumental tasks due to memory impairment. While demonstrating clear recollection at times, closer examination revealed fabricated details and disorientation regarding his current environment. Confabulation in this trilingual patient (English, German, and Arabic) posed additional challenges, necessitating a closer look at his cultural background, language barriers, and family dynamics. The patient was born and raised in Africa. He relocated recently to join his family in the US. The family was involved in managing medication adherence and therapeutic interventions. A specialist evaluated the patient, and the Montreal Cognitive Assessment (MoCA) indicated a score of 12/30, signifying moderate cognitive impairment. The assessment considered language nuances, cultural influences, and familial support. Laboratory tests were grossly unremarkable. Donepezil and Vitamin B12 were prescribed to the patient. Family members were educated regarding the use of storytelling as a therapeutic approach. Result Family involvement played a pivotal role in holistic care coordination. Despite prescribed medications, the family has not initiated treatment due to the risks of side effects. Melatonin was used as needed for sleep disturbances. Predominantly non‐pharmacological treatment approaches were implemented, including reorientation and redirection. Storytelling as a therapeutic approach was effective. Conclusion This case underscores the significance of culturally sensitive approaches in cognitive assessment and holistic care coordination, emphasizing the need for continued research on Alzheimer’s presentation in diverse populations. The abstract serves as an invitation to discuss effective strategies for diagnosing and managing Alzheimer’s dementia in culturally diverse patients, promoting inclusivity and optimal care.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".