A Rapid Neurological Assessment Protocol for Repeated Mild Traumatic Brain Injury in Awake Rats
Bibliographic record
Abstract
Preclinical models for mild traumatic brain injury (mTBI) need to recapitulate several essential clinical features associated with mTBI, including a lack of significant neuropathology and the onset of neurocognitive symptoms normally associated with mTBI. Here we show how to establish a protocol for reliably and repeatedly inducing a mild awake closed head injury (ACHI) in rats, with no mortality or clinical indications of persistent pain. Moreover, we implement a new rapid neurological assessment protocol (NAP) that can be completely conducted within 1 min of each impact. This ACHI model will help to rectify the paucity of data on how repeated mTBI (r-mTBI) impacts the juvenile brain, an area of significant concern in clinical populations where there is evidence that behavioral sequelae following injury can be more persistent in juveniles. In addition, the ACHI model can help determine if r-mTBI early in life can predispose the brain to exhibiting greater neuropathology (i.e., chronic traumatic encephalopathy) later in life and can facilitate the identification of critical periods of vulnerability to r-mTBI across the lifespan. This article describes the protocol for administering an awake closed head mTBI (i.e., ACHI) to rats, as well as how to perform a rapid NAP following each ACHI. Methods for administering the ACHI to individual subjects repeatedly are described, as are the methods and scoring system for the NAP. The goal of this article is to provide a standardized set of procedures allowing the ACHI and NAP protocols to be used reliably by different laboratories. © 2019 by John Wiley & Sons, Inc.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".