Ethical implications of shared decision-making in Parkinson’s disease treatment
Bibliographic record
Abstract
Shared decision-making (SDM) involves an active participation of the patient in deciding treatment choice, based on his/her preferences, beliefs, and values.In the context of Parkinson's disease (PD), physicians encounter limitations in applying this model related to cognitive decline and other disease-related complications.Discussing the ethics of this approach on the context of these limitations the PD patient suffers is thus of great importance.This review intends to analyze ethical challenges of SDM in PD related to decision-making capacity, surrogates' role in patient's identity, and patients' and physicians' preferences.Although skepticism could arise when dealing with surrogates' decisions, a key for flourishing the patient's autonomy is acknowledging its relational context, as relatives' beliefs and values are imprinted in the patient's identity.To do so, empathy should be encouraged in physicians, recognizing the different value attribution that patients and their relatives have in the decision process.
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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 | 0.061 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.034 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.008 | 0.008 |
| 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".