Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.434 | 0.310 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".