Verbal Episodic Memory Alterations and Hippocampal Atrophy in Acute Mild Traumatic Brain Injury
Bibliographic record
Abstract
Episodic memory deficit is a symptom frequently observed after a mild traumatic brain injury (mTBI). However, few studies have investigated the impact of a single and acute mTBI on episodic memory and structural cerebral changes. To do so, we conducted two experiments. In the first, we evaluated verbal episodic memory by using a word recall test, in 52 patients with mTBI (mean age 33.1 [12.2] years) 2-4 weeks after a first mTBI, compared with 54 healthy controls (31.3 [9.2] years) and followed both groups up for 6 months. In the second, we measured hippocampal volume in a subset of 40 participants (20 patients with mTBI, 20 controls) from Experiment 1 using magnetic resonance imaging (MRI; T1-weighted images) and correlated memory performance scores to hippocampal volume. Experiment 1 showed significantly reduced verbal episodic memory within the first month after an mTBI and a tendency for a reduction 6 months later, more pronounced for men. In Experiment 2, patients with mTBI exhibited a generally reduced hippocampal volume; however, we did not observe any linear correlation between hippocampal volume and memory scores. These results suggest that one single mTBI is associated with both episodic memory alteration and reduced volume of the hippocampus in the acute phase. Future studies are needed to elucidate the link between both measures.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".