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Record W2894257918 · doi:10.20546/ijcmas.2018.709.449

Computed Tomographic Diagnosis of Spinal Diseases in Dogs

2018· article· en· W2894257918 on OpenAlexaboutno aff
S. Heera, Ba nu

Bibliographic record

VenueInternational Journal of Current Microbiology and Applied Sciences · 2018
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Orthopedics and Neurology
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMyelographyIohexolSpinal cordSubarachnoid spaceComputed tomographicRadiologyDorsumSpinal canalContrast mediumRadiographyCalcificationNeurological deficitComputed tomographySurgeryAnatomyPathologyCerebrospinal fluid

Abstract

fetched live from OpenAlex

Computed Tomography is an noninvasive imaging method for diagnosis of vertebral and spinal cord lesions in small animals. There are retrospectively evaluated CT studies at nine dogs presented at Madras Veterinary College Teaching Hospital, Chennai with various degrees of neurological deficit and suspected with vertebral or spinal cord lesions. Out of these eight dogs, Different breeds including five Labrador retriever, one Daushand, one German shepherd and one Non-descriptive breed. All the dogs reported with neurological deficit were male. The mean age was 5.43 Yrs. CT studies were performed under general anesthesia, dorsal recumbency, without contrast medium in 4 dogs and with contrast medium Iohexol 0.22ml/Kg B.wt into subarachnoid space at occipito–atlantial junction (CT- Myelography) in 3 dogs. There were found one vertebral fracture, one vertebral fracture with luxation, three disc herniations, one extramedullary intradural tumor, one myelomalacia and one dural calcification. In conclusion CT is a valuable diagnostic tool for detection and characterization of spinal lesions.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.191
Threshold uncertainty score0.367

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 studies0.0000.001
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 head0.061
GPT teacher head0.366
Teacher spread0.304 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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