MétaCan
Menu
Back to cohort
Record W2945357404 · doi:10.21887/ijvsbt.14.4.8

Incidence and Histopathology of Sebaceous Gland Tumors in Dogs

2019· article· en· W2945357404 on OpenAlexaboutno aff
M. P. Patel, D. J. Ghodasara, Purnima B. Jani, Bhagawati Prasad Joshi, C. J. Dave

Bibliographic record

Venue˜The œIndian journal of veterinary sciences and biotechnology · 2019
Typearticle
Languageen
FieldMedicine
TopicVeterinary Oncology Research
Canadian institutionsnot available
Fundersnot available
KeywordsHistopathologySebaceous glandPathologyBiopsyIncidence (geometry)MedicineAdenomaAdenocarcinomaBreedPleomorphic adenomaCancerBiologyInternal medicineSalivary gland

Abstract

fetched live from OpenAlex

The study was aimed to know the prevalence of sebaceous gland tumor in canine and its classification based on histopathology. A total of 569 biopsy samples of canines suspected for neoplastic growth received by Department of Veterinary Pathology from TVCC departments of College of Veterinary Science and A.H. Anand over last 16 years (2001 to 2017) were analyzed. The biopsy samples were subjected to histopathological examinations by using H and E staining. Out of 569 biopsy samples, sebaceous gland growth was observed in 23 (4.04%) cases. The highest incidence (39.13%) was recorded for 9 to 12 years of age. The incidence of sebaceous gland tumor was observed in all the breeds; however, the Labrador retriever was the most affected breed followed by Pomeranian, non-descript and others. The frequent site for sebaceous gland tumor was eyelid. Out of 23 sebaceous gland tumors, one hyperplasia, 19 adenomas, two , and one adenocarcinoma were observed, suggesting that sebaceous gland adenoma was the most common type of sebaceous gland tumors in canine.

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)0.000
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 score0.439
Threshold uncertainty score0.801

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.002
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 head0.027
GPT teacher head0.319
Teacher spread0.292 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

Explore more

Same venue˜The œIndian journal of veterinary sciences and biotechnologySame topicVeterinary Oncology ResearchFrench-language works237,207