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Record W3048909151 · doi:10.15381/rivep.v31i3.18171

Frecuencia y clasificación de neoplasias orales en pacientes caninos de la Clínica de Animales Menores de la Universidad Nacional Mayor de San Marcos (2009-2013)

2020· article· es· W3048909151 on OpenAlexaboutno aff
Rosalyn Hurtado Y., Viviana Fernández P.

Bibliographic record

VenueRevista de Investigaciones Veterinarias del Perú · 2020
Typearticle
Languagees
FieldMedicine
TopicVeterinary Oncology Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHumanitiesArtGynecology

Abstract

fetched live from OpenAlex

El presente estudio tuvo como objetivo determinar la frecuencia de neoplasias orales diagnosticadas histopatológicamente en caninos pacientes de la Clínica de Animales Menores de la Facultad de Medicina Veterinaria de la Universidad Nacional Mayor de San Marcos (Lima, Perú) en el periodo enero de 2009 a diciembre de 2013. Se consideraron las variables edad, sexo, peso, raza, localización anatómica y clasificación histológica. De 238 informes histopatológicos de pacientes caninos, 192 (80.7%) fueron neoplasias y 23 (12%) fueron neoplasias orales. Las neoplasias malignas fueron las de mayor presentación (73.9%), siendo la de mayor frecuencia el melanoma oral (21.7%), mientras que la neoplasia benigna más frecuente fue el épuli (17.4%). Los machos fueron los más afectados (65.2%). El grupo etario con mayor presentación de neoplasias fue entre 6 y 10 años (52.2%) y de peso corporal entre 25 y 44 kg (47.8%). Los caninos de razas definidas fueron los más afectados (69.6%), especialmente el Rottweiler y el Labrador Retriever (13% cada raza). La localización anatómica más común fue la gingiva (69.6%).

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)0.004
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.498
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0020.003
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 head0.039
GPT teacher head0.338
Teacher spread0.299 · 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 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

Citations2
Published2020
Admission routes1
Has abstractyes

Explore more

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