A larval zebrafish model of traumatic brain injury: optimizing the dose of neurotrauma for discovery of treatments and aetiology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Traumatic brain injuries (TBI) are diverse with heterogeneous injury pathologies, which creates challenges for the clinical treatment and prevention of secondary pathologies such as post-traumatic epilepsy and subsequent dementias. To develop pharmacological strategies that treat TBI and prevent complications, animal models must capture the spectrum of TBI severity to better understand pathophysiological events that occur during and after injury. To address such issues, we improved upon our recent larval zebrafish TBI paradigm emphasizing titrating to different injury levels. We observed coordination between an increase in injury level and clinically relevant injury phenotypes including post-traumatic seizures (PTS) and tau aggregation. This preclinical TBI model is simple to implement, allows dosing of injury levels to model diverse pathologies, and can be scaled to medium- or high-throughput screening.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it