Willingness to Undergo a Risky Treatment to Improve Cognition Among Persons With Cognitive Impairment Who Received an Amyloid PET Scan
Bibliographic record
Abstract
OBJECTIVE: To evaluate determinants of willingness to accept a treatment to return memory to normal among persons with cognitive impairment who received an amyloid positron emission tomography (PET) scan and their care partner and discordance in risk taking. METHODS: Using data from CARE-IDEAS (n=1872 dyads), a supplement of the Imaging Dementia-Evidence for Amyloid Scanning study, we predicted scan recipient's willingness to accept a risky treatment, the risk care partners believed their care recipient would accept, and discordance in these perceptions. RESULTS: Scan recipients were willing to accept a treatment with a 27.94% (SD=34.36) risk of death. Care partners believed their care recipient would accept a 29.68% (SD=33.74) risk of death; thus, overestimating risk acceptance by 1.74 (SD=41.88) percentage points. A positive amyloid PET scan was associated with willingness to accept greater risk. Poorer functioning of the care recipient was associated with care partners believing their care recipient would accept more risk. The amyloid PET scan result was not significantly associated with discordance, but poorer functioning of the care recipient resulted in care partners overestimating risk. CONCLUSIONS: Scan recipients were willing to accept a treatment with a high risk of death. Discordance was affected by scan recipient's having poorer functioning.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".