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Occurrence of canine mammary and skin/ subcutaneous neoplasms in and around Thrissur district of Kerala during 2017-2020: A review of 265 cases

2021· review· en· W4205096305 on OpenAlexaboutno aff
Sudheesh S. Nair, M.K. Narayanan, Anoop Sainulabdeen, B. Dhanush Krishna, K. D. John Martin

Bibliographic record

VenueJournal of Veterinary and Animal Sciences · 2021
Typereview
Languageen
FieldMedicine
TopicVeterinary Oncology Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSubcutaneous fatSkin tumoursTrunkVeterinary medicineInternal medicinePathologyBiology

Abstract

fetched live from OpenAlex

The study was conducted in 265 clinical cases of mammary and skin/ subcutaneous neoplasms in dogs presented to University Veterinary Hospitals Mannuthy and Kokkalai during a period of 36 months from October 2017 to September 2020. Mammary neoplasms were found more in females (51.7 per cent) than in males whereas skin and subcutaneous neoplasms were found more in male dogs (48.3 per cent). The maximum occurrence of neoplasms was recorded in the age group of eight to twelve years (38.5 per cent) whereas least occurrence was noticed in the age group up to four years (9 per cent). Labrador and Rottweiler breeds were found more affected with neoplasms (38 per cent each) with highest occurrence of mammary neoplasms in inguinal mammary glands (35.03 per cent) and highest occurrence of skin/ subcutaneous neoplasms on sites involving trunk region (14.06 per cent cases). Eighty-three per cent of the neoplasm cases in the present study were pet dogs with a greater number of dogs maintained in outdoor kennels and 17 per cent of neoplasm was found in free-roaming dogs rescued from streets. Out of total 265 dogs, 37.73 per cent dogs were found to be having commercial dog food as their main feed and 32 per cent dogs were fed with a mixed diet of homemade food and commercial dog food. Among the cases, 14.71 per cent dogs had a previous history of cancer surgery

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: Case report · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.657
Threshold uncertainty score0.712

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
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 head0.126
GPT teacher head0.431
Teacher spread0.305 · 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 designCase report
Domainnot available
GenreReview

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

Citations4
Published2021
Admission routes1
Has abstractyes

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