Assessment of decisional capacity: Prevalence of medical illness and psychiatric comorbidities
Bibliographic record
Abstract
OBJECTIVE: Studies on decisional capacity have primarily focused on cognitive disorders, whereas noncognitive disorders remain understudied. The purpose of our study was to assess decisional capacity across a wide spectrum of medical and psychiatric disorders. METHOD: More than 2,500 consecutive consults were screened for decisional capacity, and 336 consults were reviewed at Bellevue Hospital Center in New York. Sociodemographic and medical variables, medical and psychiatric diagnoses, as well as decisional capacity assessments were recorded and analyzed. RESULTS: Consults for decisional capacity were most commonly called for in male patients with cognitive and substance abuse disorders. Less commonly, consults were called for patients with mood or psychotic disorders. Overall, about two thirds of patients (64.7%) were deemed not to have decisional capacity. Among medical diagnoses, neurological disorders contributed to decisional incapacity, and among the psychiatric diagnoses, cognitive disorders were most frequently documented in cases lacking decisional capacity (54.1%) and interfered more commonly with decisional capacity than substance abuse or psychotic disorders (37.2 and 25%). In contrast, patients with mood disorders usually retained their decisional capacity (32%). Generally, the primary treatment team's assessment was accurate and was confirmed by the psychiatric service. SIGNIFICANCE OF RESULTS: Although decisional capacity assessments were most commonly requested for patients with substance abuse and cognitive disorders, the latter generally affected the ability to make healthcare decisions the most. Further, cognitive disorders were much more likely to impair the ability to make appropriate healthcare decisions than substance abuse or psychotic disorders.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".