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Record W2499207012 · doi:10.1007/978-1-60761-750-1_14

Behavioral Testing in Rodent Models of Stroke

2010· book-chapter· en· W2499207012 on OpenAlexaff
Gerlinde A. S. Metz

Bibliographic record

VenueNeuromethods · 2010
Typebook-chapter
Languageen
FieldNeuroscience
TopicMemory and Neural Mechanisms
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsStroke (engine)PsychologyPhysical medicine and rehabilitationRodent modelCognitionMedicineNeuroscience

Abstract

fetched live from OpenAlex

The critical test of a therapeutic intervention is whether it affects clinically relevant outcomes. Reliable tests of functional outcome, therefore, represent a vital part of preclinical stroke research. This chapter presents select behavioral tests commonly used for evaluating somatosensory, locomotor, skilled, and cognitive functions in rodent models of stroke. The methods described emphasize the value of careful quantitative and qualitative assessment of acute and long-term behavioral deficits. Some of the protocols presented allow us to determine whether a preclinical treatment restores the original function or simply enhances the performance by improving the learning of alternative strategies. Recommendations are given for the selection of tests designed to detect chronic deficits and subsequent improvements in experimental stroke.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.006

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 head0.391
GPT teacher head0.403
Teacher spread0.012 · 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 designNot applicable
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

Citations5
Published2010
Admission routes1
Has abstractyes

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