Characterizing the Window of Vulnerability in a Mouse Model of Mild Traumatic Brain Injury
Bibliographic record
Abstract
Following one mild traumatic brain injury (mTBI), there is a window of vulnerability during which subsequent mTBIs can cause exacerbated impairments. I characterized this window in mice by giving them one or two mTBIs separated by various intervals and measuring their resulting behavioural impairment using the Y-maze, visual cliff, and novel object recognition tests as well as their white matter pathology by silver staining. The window of vulnerability following our lab’s mTBI model was over 2 weeks when defined by silver staining but was not detectable when defined by behavioural testing. I also found that sex and impact severity differentially affect post-mTBI vulnerability in different white matter regions. This work highlights the value of including white matter damage, sex, and replicable mTBI models for the study of post-mTBI vulnerability. As well, this work establishes important groundwork for the future investigation of mechanisms, biomarkers, and therapies for post-mTBI vulnerability.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".