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Record W1983621019 · doi:10.3791/51820

A Novel Model of Mild Traumatic Brain Injury for Juvenile Rats

2014· article· en· W1983621019 on OpenAlexafffund
Richelle Mychasiuk, Allyson Farran, Mariana Angoa‐Pérez, Denise I. Briggs, Donald M. Kuhn, Michael J. Esser

Bibliographic record

VenueJournal of Visualized Experiments · 2014
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversity of CalgaryAlberta Children's Hospital
FundersAlberta Children's Hospital Research InstituteAlberta Children's Hospital FoundationChildren's Hospital FoundationAmerican College Health FoundationUniversity of CalgaryU.S. Department of Veterans Affairs
KeywordsConcussionTraumatic brain injuryPhysical medicine and rehabilitationJuvenileAnimal modelRodent modelMedicineHead injuryPoison controlInjury preventionNeurosciencePsychologySurgeryBiologyEmergency medicinePsychiatryInternal medicine

Abstract

fetched live from OpenAlex

Despite growing evidence that childhood represents a major risk period for mild traumatic brain injury (mTBI) from sports-related concussions, motor vehicle accidents, and falls, a reliable animal model of mTBI had previously not been developed for this important aspect of development. The modified weight-drop technique employs a glancing impact to the head of a freely moving rodent transmitting acceleration, deceleration, and rotational forces upon the brain. When applied to juvenile rats, this modified weight-drop technique induced clinically relevant behavioural outcomes that were representative of post-concussion symptomology. The technique is a rapidly applied procedure with an extremely low mortality rate, rendering it ideal for high-throughput studies of therapeutics. In addition, because the procedure involves a mild injury to a closed head, it can easily be used for studies of repetitive brain injury. Owing to the simplistic nature of this technique, and the clinically relevant biomechanics of the injury pathophysiology, the modified weight-drop technique provides researchers with a reliable model of mTBI that can be used in a wide variety of behavioural, molecular, and genetic studies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0070.003

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.

Opus teacher head0.199
GPT teacher head0.513
Teacher spread0.314 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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".

Quick stats

Citations85
Published2014
Admission routes2
Has abstractyes

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