Decision-making deficits persist after aneurysmal subarachnoid hemorrhage.
Bibliographic record
Abstract
OBJECTIVE: Effective decision-making is critical for resuming day-to-day activities after aneurysmal subarachnoid hemorrhage (aSAH). Little is known, however, about how decision-making is affected after aSAH, particularly under ambiguous conditions in which neither the outcome nor the outcome probabilities are known. METHOD: Here we examined the integrity of decision-making under ambiguity in a cohort of aSAH patients classified as having made a "good outcome" according to the Glasgow Outcome Scale. Thirty aSAH survivors and 33 healthy controls completed the Iowa Gambling Task (IGT) and the Balloon Analogue Risk Task (BART). Mean time of assessment poststroke was 30 months. RESULTS: Although patients and controls had similar decision-making strategies on the IGT, patients made significantly fewer switches between decks, suggesting perseveration and cognitive inflexibility. On the BART, aSAH patients demonstrated significantly enhanced risk-taking behavior relative to controls. Examination of effect sizes revealed cognitive inflexibility in 33% to 35% of aSAH patients and enhanced risk-taking behavior in 35% to 40% of aSAH patients. CONCLUSION: Approximately one third of "good outcome" aSAH patients experience cognitive inflexibility and enhanced risk-taking behavior over 2 years poststroke, illustrating the persistence of aSAH-associated cognitive impairment.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".