Effects of mild traumatic brain injury cannot be differentiated from substance abuse
Bibliographic record
Abstract
PRIMARY OBJECTIVE: Patients involved in litigation relating to mild traumatic brain injury (MTBI) typically undergo a forensic neuropsychological evaluation. However, if cognitive problems are identified, it is difficult to know whether these are related to the MTBI or other factors such as pre- and/or post-injury substance abuse. The purpose of this study was to compare the neuropsychological test performances of 73 patients with acute, uncomplicated MTBIs to a sample of 73 patients from an inpatient substance abuse programme. METHODS AND PROCEDURES: Patients were perfectly matched on age, education and gender. Ten cognitive measures were used that included the Trail Making Test (TMT) and selected sub-tests from the Wechsler Memory Scale-Revised. MAIN OUTCOMES AND RESULTS: Patients with MTBI demonstrated poorer performances on Digits Backwards (p < 0.028) and TMTA (p < 0.032). There were no significant differences between the two groups on the remaining cognitive measures. The clinical usefulness of these measures to differentiate between the groups, using discriminant function analysis, was very poor. CONCLUSIONS: Patients with uncomplicated MTBIs could not be reliably differentiated from patients with substance abuse problems on these measures of concentration, memory and processing speed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".