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Record W3012207854 · doi:10.1523/eneuro.0488-19.2020

An Open Source Automated Bar Test for Measuring Catalepsy in Rats

2020· article· en· W3012207854 on OpenAlexafffund
Karling R. Luciani, Jude A. Frie, Jibran Y. Khokhar

Bibliographic record

VenueeNeuro · 2020
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neuropharmacology Research
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsCatalepsyMicrocontrollerBar (unit)Computer scienceEmbedded systemSoftwareSimulationComputer hardwareHaloperidolOperating systemNeuroscience

Abstract

fetched live from OpenAlex

Catalepsy bar tests are widely used to measure the failure to correct an imposed posture resulting from muscular rigidity. Procedures for measuring catalepsy vary greatly in the published literature, but one commonly used test measures the time it takes for a rodent to remove one or both of its forelimbs from a bar. The following paper describes an affordable, adjustable, open-source bar test that automatically measures and logs the time it takes for a rat to remove itself from a bar. While commercially available automated bar tests are prohibitively expensive, requiring proprietary software and hardware to operate, the proposed apparatus runs on an Arduino-based microcontroller making it low-cost and customizable. This 3D-printed design costs less than 65 United States dollars to build and is simple to assemble and operate. The beam-break sensor design also eliminates many of the pitfalls of the "complete-the-circuit"-based approach to recording catalepsy. The paper further describes the successful validation of the design using adult male rats injected with different doses of haloperidol to demonstrate a dose-dependent cataleptic effect. This design provides a versatile, low-cost solution to standardizing and automating measurement of catalepsy in rodents.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.695

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.159
GPT teacher head0.393
Teacher spread0.234 · 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 teacher head, not a consensus.

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

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

Citations25
Published2020
Admission routes2
Has abstractyes

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