Complicated vs uncomplicated mild traumatic brain injury: Acute neuropsychological outcome
Bibliographic record
Abstract
PRIMARY OBJECTIVE: The purpose of this study was to carefully examine the effects of a complicated vs uncomplicated mild traumatic brain injury (MTBI) on acute neuropsychological outcome. RESEARCH DESIGN: Participants were derived from an archival trauma database. This is a retrospective matched groups design. METHODS AND PROCEDURES: All patients were seen through a Head Injury Trauma Service clinical pathway. To be included, all patients must have undergone a day-of-injury CT scan and completed a small battery of neuropsychological tests within 2 weeks of injury. Patients were sorted into two groups on the basis of having a normal or abnormal CT scan. Patients were then carefully matched on age, education, gender and mode of injury (e.g. car accident, fall or assault). The final sample consisted of 100 patients, with 50 in each group. MAIN OUTCOMES AND RESULTS: The patients with complicated MTBIs performed significantly more poorly on some of the neuropsychological tests. However, the effect sizes were small or medium and the two groups could not be differentiated using logistic regression analysis. CONCLUSIONS: The reasons why people recover slowly or fail to recover fully from MTBIs remain poorly understood. Visible structural brain damage carries increased risk for slow and incomplete recovery, but certainly does not provide an explanation for good or poor outcome in the majority of patients.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.005 | 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".