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Record W2963333652

Normal intraocular echo-biometric indices of adult dogs

2018· article· en· W2963333652 on OpenAlexaboutno aff
D.M. Tripathi, Virender Malik, Ajeet Singh, Rahul Pandey

Bibliographic record

VenueIndian Journal of Veterinary Surgery · 2018
Typearticle
Languageen
FieldMedicine
TopicCorneal surgery and disorders
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineOphthalmologyGerman Shepherd DogVitreous chamberUltrasoundLabrador RetrieverAnatomyNuclear medicineSurgeryEye diseaseRadiologyRefractive error
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this study was to describe the ultrasonographic appearance and to measure different intraocular echo-biometric indices in normal adult dogs. B-mode transcorneal ultrasonographic scanning of left and right eyes of six healthy adult dogs each from three different breeds viz. German shepherd, Labrador retriever and Indian mongrels were performed. Qualitative echo-biometric findings of the eyes were described and measurements of the intraocular structures were obtained. In the present transcorneal intraocular echobiometric study six parameter were measured, i.e., aqueous chamber depth (ACD), lens depth (LDe), lens diameter (LDi), vitreous depth (VD), sclero-retinal rim thickness (SRT), and globe axial length (GAL) by using high end ultrasound machine (Mylab30vet), with 2.5–7.5 MHz microconvex transducer and the depth of scanning was set at 5–9 cm with suitable gain without administration of any general/local anaesthetic. Nonsignificant difference (P>0.05) was observed in all parameters when compared between left and right eye of different breeds of animals. The average values of LDi and GAL of both eyes of German shepherd dog were significantly different from Labrador retriever and Indian mongrel dogs. The average value of SRT of both eyes of German shepherd and Labrador were significantly higher (P 0.05) among three breeds of dog.

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)0.001
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 score0.170
Threshold uncertainty score0.665

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.028
GPT teacher head0.282
Teacher spread0.254 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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