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Muscular Dystrophy in female Dogs

2001· article· en· W4239045751 on OpenAlexaff
G. Diane Shelton, Ling A. Liu, Ling T. Guo, Gail K. Smith, Jeffrey S. Christiansen, William B. Thomas, Mary O. Smith, Karen L. Kline, Philip A. March, Thomas Flegel, Eva Engvall

Bibliographic record

VenueJournal of Veterinary Internal Medicine · 2001
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsSmiths Detection (Canada)
FundersNational Institutes of HealthMuscular Dystrophy Association
KeywordsDystrophinMuscular dystrophyMedicineMuscle biopsyPathologyWastingLamininMyopathyEndocrinologyInternal medicineBiopsyGeneticsBiology

Abstract

fetched live from OpenAlex

Abstract The most common form of muscular dystrophy in dogs and humans is caused by mutations in the dystrophin gene. The dystrophin gene is located on the X chromosome, and, therefore, disease-causing mutations in dystrophin occur most often in males. Therefore, females with dystrophin deficiency or other forms of muscular dystrophy may be undiagnosed or misdiagnosed. Immunohisto-chemistry was used to analyze dystrophin and a number of other muscle proteins associated with muscular dystrophy in humans, including sarcoglycans and laminin α2, in muscle biopsy specimens from 5 female dogs with pathologic changes consistent with muscular dystrophy. The female dogs were presented with a variety of clinical signs including generalized weakness, muscle wasting, tremors, exercise intolerance, gait abnormalities, and limb deformity. Serum creatine kinase activity was variably high. One dog had no detectable dystrophin in the muscle; another was mosaic, with some fibers normal and others partly dystrophin-deficient. A 3rd dog had normal dystrophin but no detectable laminin α2. Two dogs could not be classified. This study demonstrates the occurrence of dystrophin- and laminin α2-associated muscular dystrophy and the difficulty in clinical diagnosis of these disorders in female dogs.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.952
Threshold uncertainty score0.335

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.020
GPT teacher head0.308
Teacher spread0.288 · 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

Citations58
Published2001
Admission routes1
Has abstractyes

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