Diagnosis Disclosure of Prodromal Alzheimer Disease-Ethical Analysis of Two Cases
Bibliographic record
Abstract
BACKGROUND: According to a recent proposal for revised diagnostic criteria for Alzheimer disease, the diagnosis could be made even in the absence of impairment of social function or daily life activities, provided positivity of one or more abnormal biomarkers. The use of the new proposed diagnostic criteria raises ethical issues and needs to be carefully evaluated. METHOD: We describe two clinical cases of prodromal Alzheimer's disease and discuss the diagnosis disclosure, taking into consideration several issues: (i) the issue of the boundary between well founded research procedures and clinical practice, (ii) the issue of the fuzziness of the concepts of scientific evidence and scientific uncertainty, (iii) the issue of patient's autonomy and patient's best interest, and (iv) the issue of the patients' specific personal and social context. RESULTS: The degree of informativeness of the proposed diagnostic criteria for the single patient is already such as to deserve high regard in making the diagnosis and in the diagnosis disclosure process. During the disclosure process, the physician needs to take into account both what is known and what it is not sufficiently known. The patient's personal and environmental conditions should drive the physician to partial or full diagnostic disclosure, or delay communication. CONCLUSION: We proposed two different diagnosis disclosure processes, on the basis of the common neurological features and of the different global clinical situations, socio-personal contexts and attitudes towards the communication of the diagnosis.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".