Deficits in complex visual information processing after mild TBI: Electrophysiological markers and vocational outcome prognosis
Bibliographic record
Abstract
PRIMARY OBJECTIVE: To evaluate low-level to complex information processing using visual electrophysiology and to examine the latter's prognostic value in regards to vocational outcome in persons having sustained a mild traumatic brain injury (mTBI). RESEARCH DESIGN/METHODS: Event-related potentials (ERPs) were recorded to pattern-reversal, simple motion, texture segregation and cognitive oddball paradigms from 17 participants with symptomatic mTBI at onset of specialized clinical intervention and from 15 normal controls. The relationship between abnormal electrophysiology and post-intervention return to work status was also examined. MAIN OUTCOMES AND RESULTS: Participants with mTBI showed a statistically significant (p<0.05) amplitude reduction for cognitive ERPs and delayed latencies for texture (p<0.05) and cognitive paradigms (p<0.005) compared to controls. Furthermore, participants with mTBI presenting texture or cognitive ERP latency delays upon admission were at significantly (p<0.01) greater risk of negative vocational outcome than mTBI participants with normal electrophysiology. CONCLUSIONS: The findings suggest that individuals with symptomatic mTBI can present selective deficits in complex visual information processing that could interfere with vocational outcome. ERP paradigms such as those employed in this study thus show potential for evaluating outcome prognosis and merit further study.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".