Neuropsychological functioning following complicated vs. uncomplicated mild traumatic brain injury
Bibliographic record
Abstract
OBJECTIVE: It would be logical to assume that patients with intracranial abnormalities (i.e. complicated MTBIs) would have worse outcome than patients without these abnormalities (i.e. uncomplicated MTBIs). However, the literature is limited and somewhat mixed regarding outcome in patients with complicated mild TBIs. The purpose of this study is to employ a carefully controlled research design to compare the acute neuropsychological functioning of patients following complicated and uncomplicated MTBI. METHOD: Participants were 20 patients with complicated MTBI and 20 patients with uncomplicated MTBI selected from an archival database of 465 patients. Patients were carefully matched on age, education, gender, ethnicity, days assessed post-injury and mechanism of injury. Patients were assessed an average of 3.5 days (SD = 1.9) post-injury with 13 common cognitive variables. RESULTS: There were significant group differences on only three of the 13 cognitive measures (complicated mild TBI worse than uncomplicated mild TBI). There were no significant differences in the proportion of impaired scores between groups on all measures, with the exception of Hopkins Verbal Learning Test Delayed Recall. CONCLUSION: Patients with complicated MTBIs performed more poorly only on a small number of tests during the acute recovery period.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".