Author's reply to: Difficulties of diagnosing and managing dementia in people with Down syndrome
Bibliographic record
Abstract
Difficulties of diagnosing and managing dementia in people with Down syndromeWe thank Drs Smith and Chicoine for their interest in our work and highlighting some of the practical issues in diagnosing and managing dementia in this group.The association between trisomy 21 and early-onset Alzheimer's disease is well established 1 and dementia is now the most common cause of death in adults with Down syndrome.Despite this, there exists relatively little evidence on which to base treatment decisions.Using a naturalistic study design, we report the effects of antidementia medication on the survival and function of 310 people with Down syndrome and dementia.Notwithstanding the limitations typical of observational studies (discussed in the paper), this work addresses a significant gap in the literature.Kaplan-Meier survival curves demonstrate significantly increased survival in the group prescribed antidementia medication.Baseline differences between those prescribed and not prescribed antidementia medication were accounted for, where possible, in a Cox regression model.This adjusted analysis showed that protection in the treated group remained, although it did not reach statistical significance because of less power and broader confidence intervals.Functional impairment was measured using the Dementia in Learning Disabilities scale, 2 a standardised informant questionnaire that covers several skill domains.These data show an early protective effect of medication in mitigating cognitive decline, as is observed in individuals with Alzheimer's disease without Down syndrome.3 We appreciate the concern of Drs Smith and Chicoine for quality of life.Unfortunately, there are no well-validated measures of quality of life for this group and proxy measures have been subject to limitations in people with intellectual disability.Development of such measures and their use in research studies and routine clinical care would be welcome and could focus efforts on providing optimal holistic support.The Cochrane reviews that Drs Smith and Chicoine cite highlight the lack of evidence in this field, rather than negative results of drug intervention studies.Two of these Cochrane reviews did not include any studies at all, and the third included only one, small randomised controlled trial.The authors of these reviews, now some years old, highlight the paucity of evidence and conclude that the reviews cannot be used to guide practice.Our cohort was recruited from specialist memory clinics for people with intellectual disability.Clinician diagnosis of dementia in such clinics is valid and reliable 4 and we are confident that clinicians will have adequately assessed potentially reversible causes of decline.It is important not to overlook dementia as an early diagnosis can facilitate prompt pharmacological and psychosocial treatments and effective care planning.5 When dementia is diagnosed, a decision to use medication is, of course, an individual one, and should take account of the views of families and carers.Our paper provides additional evidence that could inform the decision-making process.People with Down syndrome and dementia should not be denied access to antidementia drugs.
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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.032 | 0.024 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".