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Record W7132867949

Development of a Rat Model to Characterize the Effects of Ischaemia on the Masseter and Temporalis Muscles

2021· dissertation· W7132867949 on OpenAlexaff
D A Makar

Bibliographic record

VenueTSpace · 2021
Typedissertation
Language
FieldHealth Professions
TopicTemporomandibular Joint Disorders
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMasticatory forceIschemiaMasticationMasseter muscleRat modelTemporal muscleFibrosisAnimal model
DOInot available

Abstract

fetched live from OpenAlex

Temporomandibular disorders of the muscles of mastication (mTMD) are associated with muscle ischaemia. The objective of this study was to develop a rat model to characterize the effects of ischaemia on masticatory muscles.The right external carotid arteries of 18 Sprague-Dawley rats were ligated to induce masticatory muscle ischaemia. The animals were euthanized at 10-, 20-, and 35- days post surgery. Right and left masseter and temporalis muscles were evaluated by quantitative sensory testing, histological and gene expression analyses. Data were analyzed using mixed and general linear models. Mechanical detection thresholds increased with time and were greater on the right than the left side after 20-days. Histological changes indicative of fibrosis and degeneration of the right masticatory muscles were evident at all timepoints and complemented by gene expression analyses. This novel model of masticatory muscle ischaemia holds promise for future studies of the role of ischaemia in mTMD.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.065
GPT teacher head0.391
Teacher spread0.327 · 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
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

Citations0
Published2021
Admission routes1
Has abstractyes

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