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Record W2408184693 · doi:10.1111/vop.12067

Preface

2013· article· en· W2408184693 on OpenAlexaff
Bruce H Grahn, Richard Dubielzig

Bibliographic record

VenueVeterinary Ophthalmology · 2013
Typearticle
Languageen
FieldMedicine
TopicVeterinary Oncology Research
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsSubspecialtyMedicineOphthalmologyExcellenceVeterinary pathologySuspectPathologyPsychology

Abstract

fetched live from OpenAlex

In this Special Issue of Veterinary Ophthalmology, there are a number of papers that represent the wide and varied subspecialty of Veterinary Ocular Pathology. The subspecialty of ocular pathology is shared between pathologists and ophthalmologists with each group bringing a much needed set of skills and experience to the table. It was in this spirit, drawing on Dr. Dubielzig's credentials as a pathologist and Dr. Grahn's as an ophthalmologist, both with strong interests in Ocular Pathology, that we served as Guest Editors. Dr. Dubielzig and I have collaborated in the classroom and lecture halls on several occasions, and these exchanges have always been fun, and this was no exception. Many of our colleagues and friends have influenced the development of Ocular Pathology, and some have authored papers in this Special Edition. Few would argue that an enhanced understanding of ocular pathology is essential to achieving excellence in the ophthalmic examination and surgery rooms. Therefore, we hope to see a continued emergence of Ocular Pathology as a subspecialty in the field of Veterinary Ophthalmology and Pathology. As evidence of the growth of this subspecialty, the first annual meeting of the Comparative Ocular Pathology Society (COPS) will occur in Madison, WI September 26–28, 2013. This special issue of Veterinary Ophthalmology offers the reader case reports, retrospective case series, histologic research studies, and review articles. The case reports include several first or second time reports in a diverse number of species which include intraocular myelolipoma in a dog, suspect primary histiocytic ocular sarcoma in a cat, post-traumatic intraocular sarcoma in a rabbit, metastatic schwannoma of a blue-eyed dog, squamous cell carcinoma transformation from a eyelid papilloma, conjunctiva squamous cell carcinoma in a Reindeer, anterior segment dysplasia in two snow leopards, intraocular invasion of squamous cell carcinomas in cats, immunodetection techniques for coronavirus detection in cats with ocular FIP via skin lesion biopsy, orbital myofibroblastic sarcoma diagnostic evaluations and therapy in a cat, ocular liposarcoma in a Guinea pig, B-cell optic nerve lymphoma in a young dog, tapetal dysplasia in a Vallhund dog, and a primary conjunctival lymphoma in a dog. Retrospective cases series include histopathologic evaluation of anterior segments of globes that have developed glaucoma secondary to lens luxation, primitive neuroectoderm intraocular tumors, and histologic findings of globes and evisceration contents of dogs that developed glaucoma after cataract surgery. Research papers include an investigation of the presence of Mast cells within the choroid of animals, and the anatomic features of Cetacean globes. The review articles include Animal ocular models for translational research, and Ocular neoplasms of dogs. We trust this Special Edition on ocular pathology will meet your expectations, and the articles will be thought provoking, and useful to both ophthalmologists and pathologists. Both of us have enjoyed putting this Special Edition together, and we personally thank the many authors who responded to our encouragement and deadlines. For those whose articles missed the submission deadline, do not lament, once accepted they will appear in subsequent issues of Veterinary Ophthalmology insuring a continuum of ocular pathology manuscripts for our readers. Those of us participating in ocular pathology are very appreciative of the welcome reception that our submissions continue to receive from this journal.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.872
Threshold uncertainty score0.995

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)0.0100.005

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 head0.095
GPT teacher head0.391
Teacher spread0.296 · 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; both teacher heads agree on what is shown here.

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
Published2013
Admission routes1
Has abstractyes

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