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Record W2054288980 · doi:10.2466/pms.2003.97.3f.1115

Mechanical Impacts to the Skulls of Rats Produce Specific Deficits in Maze Performance and Weight Loss: Evidence for Apoptosis of Cortical Neurons and Implications for Clinical Neuropsychology

2003· article· en· W2054288980 on OpenAlexaff
Wudu E. Lado, Michael A. Persinger

Bibliographic record

VenuePerceptual and Motor Skills · 2003
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsLaurentian University
Fundersnot available
KeywordsCerebrumNeuroscienceApoptosisNeuropsychologyStimulationOpen fieldCerebral cortexDorsumMedicinePathologyPsychologyPhysiologyAnatomyBiologyInternal medicineCentral nervous systemCognition

Abstract

fetched live from OpenAlex

The purpose of this experiment was to induce closed head injuries that might be applicable to clinical neuropsychology. Six adult female albino rats were struck over the right dorsal skull by a 200-gm weight that fell through a 0.9-m tube while another six rats served as controls. The rats that received the impact to the skulls displayed significantly more weight loss and fewer completions of the maze during the subsequent two to four days (effect size about 40%) while their open field behaviors, response latencies to thermal stimulation of the feet, and immobility within a conditioned fear setting did not differ significantly from those of controls. Histological analyses of the brains about 35 days after the impact indicated striking alterations in the morphology of cerebral cortical neurons, strongly suggestive of an apoptotic-like process, within the dorsal cerebral cortices below the likely impact site. Distributions of clusters of these aberrant-looking cells were also evident opposite to the impact site within the ventral cerebrum. Because apoptosis is involved with minimal inflammation and edema, detection of diffuse apoptosis by MRI and CT would be unlikely even though the influence on adaptability would be significant.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.518
Threshold uncertainty score0.416

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.164
GPT teacher head0.425
Teacher spread0.261 · 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 designObservational
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

Citations8
Published2003
Admission routes1
Has abstractyes

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