Bibliographic record
Abstract
Forgetting is bad. Remembering is always good. Or so I thought until I met patients with unexpected benefits of memory loss. “It will be a blessing if I lose my mind,” one patient said. “I'll probably be happy not knowing I owe anybody!” Andy Rooney wrote, in his 1982 book, And More by Andy Rooney, “Sadness is one of the principal ingredients of memory, and there's just so much of that [that] anyone wants to bring on himself on purpose by sitting around remembering.” All this raises an important question in my mind. Is forgetfulness nature's way of enforcing forgiveness, the letting go of past wrongs and sorrows, the memory of which can only sadden the ageing mind, with little chance for reconciliation and closure? In pondering this question I wonder whether the evolutionary reason for the high prevalence of Alzheimer's disease among those aged 80 or older is to shield ageing minds from the vagaries of the past, the company of friends and family severed, the sadness that comes with dreams unfulfilled. Was I being too picky in correcting the medical student who introduced his 91 year old patient as being “pleasantly demented with no complaints”? My premise—that there is nothing pleasant about Alzheimer's disease—conflicts with occasional sightings of apparently happy patients living with the disease. Maybe the medical student is right after all. Will memory enhancement therapy make these patients happier or more agitated and less functional? If the student is right, will memory enhancement therapy make these patients happier or more agitated and less functional? To answer this question I rely on my recent clinical observations: achieving evidence based treatment goals of improved cognition may not always match the goals of success defined by patients and their carers. I have also become more aware of the importance of constant re-evaluation of and listening to the treatment goals expressed by patients and their carers, whose priorities may not fit neatly into our evidence based treatment guidelines for cognition improvement treatment. A number of unexpected symptoms may emerge as cognition improves in response to treatment: reliving a painful stage of Alzheimer's disease already surpassed, unmasking of depression and anxiety, and re-experiencing of a long forgotten bereavement and dysphoria. Perhaps an example from my practice may better illustrate these unexpected symptoms. A recently widowed 78 year old patient with Alzheimer's disease was coping well with her daily living activities, with some help from her sister. She received a cholinesterase inhibitor for six months. She became more despondent, lost weight, and became increasingly agitated. In her newly observed delusion, she spent hours having heated arguments with her deceased husband. Clinical assessment and investigations showed no obvious cause. In line with the family's request the treatment was stopped. Within three months her appetite improved, her delusion resolved, and she became less despondent and agitated. Perhaps the sadness and sorrow of bereavement are not worth remembering. The case for using drugs to treat Alzheimer's disease is strong: temporary improvements in cognition and function, with some palliation of behavioural disturbances (BMJ 2001;323: 123-4 [PubMed]). However, when the clinician's goals no longer match the values and preferences of patients and their families, we are in violation of the principle of “first do no harm.” With new treatments and better diagnostic tools on the horizon, it becomes even more imperative that patients' values, expectations, and preferences are at the centre of the treatment decision making process. In a book review Dr Peter Whitehouse, who also happened to have Alzheimer's disease, said: “Between normality and dementias such as Alzheimer's disease there are more hilly, continuous relations than chasms of categorical distinctions. Alzheimer's disease is a social marker, and those who control this marker are powerful” (New England Journal of Medicine 2001;347: 861). With this in mind, we should constantly ask the question, when choosing anti-dementia treatments, of what is in the best interest of our patients and their loved ones. Adhering to the principle of “first do no harm” is following evidence based guidelines—such as those published by the Canadian consensus conference on dementia (Canadian Journal of Neurological Sciences 2001;28(suppl 1): S3-16 [PubMed]) and the American Academy of Neurology (Neurology 2001;56: 1154-66 [PubMed])—while being aware of the potential but unexpected “hazards” of good memory.
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 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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.100 | 0.034 |
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".