Making Decisions and Advising Decisions in Traumatic Brain Injury
Bibliographic record
Abstract
OBJECTIVE AND BACKGROUND: Decision under ambiguity and decision under risk are fundamental in every-day life. METHODS: We investigated these 2 types of decision in traumatic brain injury (TBI) patients through the Iowa Gambling Task (IGT), the Probability-Associated Gambling (PAG) task, and a counsel version of the PAG task. Although in the IGT rules for gain and losses are implicit and probability information is missing, in the PAG task and the counsel task rules are explicit and probabilities are well-defined. RESULTS: In the IGT, TBI patients selected more disadvantageously than healthy controls and failed to develop an advantageous strategy over time. Patients also made less advantageous choices than controls in the PAG task and the counsel task. Compared with controls, TBI patients gambled more frequently with low probabilities and less frequently with high probabilities. Overall, participants decided more advantageously in the counsel task, which does not provide feedback, than in the PAG task. Importantly, our results indicate that TBI patients' performance on all decision tasks correlated with executive functions. CONCLUSIONS: Our study shows that TBI patients have difficulties in decision under risk and decision under ambiguity. Difficulties may be attributed to deficient learning from feedback and to reduced risk estimation, but not to impulsive risk taking behavior.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".