Psychological impact of biomarker-assisted diagnosis of Alzheimer's disease
Bibliographic record
Abstract
BackgroundCerebrospinal fluid biomarkers can be used to diagnose biological Alzheimer's disease (AD). The psychological safety of this approach is often questioned given the paucity of effective therapies for AD.ObjectiveWe wanted to evaluate the psychological impact of biomarker assisted diagnosis on patients and their caregivers.MethodsUsing a mixed method design, 10 patients and 16 caregivers were evaluated before and after receiving a diagnosis of AD with this technique. Interviews were conducted to explore their perspectives. Questionnaires were used to evaluate the effects on quality of life, depression and caregiver burden.ResultsParticipants mentioned themes like having an objective explanation for their symptoms, being able to act on the information, finding lumbar puncture invasive, and receiving a difficult diagnosis with little possible action. Most participants said they would recommend the procedure. Measures of quality of life, depression and caregiver burden were similar before and after disclosure.ConclusionsThis study suggests that, from a psychological perspective, cerebrospinal fluid-based diagnosis of AD could be offered to patients after discussion of potential benefits and risks. Further research is needed in this field, especially as new diagnostic methods become more available.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".