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Record W2980157442 · doi:10.15381/rivep.v30i3.16592

Frecuencia de neoplasias en glándula mamaria de caninos diagnosticadas histopatológicamente en la Facultad de Medicina Veterinaria de la Universidad Nacional Mayor de San Marcos, periodo 2007- 2016

2019· article· es· W2980157442 on OpenAlexaboutno aff
Julisa Lipa C., Rosa Perales C., Viviana Fernández F., Gilberto Santillán A., César Gavidia C.

Bibliographic record

VenueRevista de Investigaciones Veterinarias del Perú · 2019
Typearticle
Languagees
FieldMedicine
TopicVeterinary Oncology Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGynecology

Abstract

fetched live from OpenAlex

El objetivo de este estudio retrospectivo fue determinar la frecuencia de neoplasias en la glándula mamaria de caninos cuyo diagnóstico histopatológico se realizó en el Laboratorio de Histología, Embriología y Patología Animal de la Facultad de Medicina Veterinaria de la Universidad Nacional Mayor de San Marcos partir de los informes recopilados de 2007 hasta 2016, tomándose en cuenta las variables sexo, raza, edad, ubicación y diagnósticos histopatológicos. Las láminas fueron clasificadas según el sistema establecido por Goldschmidt et al. (2011). El total de procesos neoplásicos en caninos fue 1599, donde 359 correspondieron a neoplasias de glándula mamaria (22.4 ± 2.04% IC0.95). Las hembras fueron las más afectadas (98%), mientras que la proporción de canes mestizos afectados fue de 32%. El 68% se presentó en razas puras, principalmente Cocker (13%), Bóxer (5.9%), Labrador (5.4%) y Yorkshire (4.5%). Canes de 10 años fueron los más afectados (17.7%), siendo el estrato etario entre 9 y 12 años el más afectado (49.3%). El 60.6% de las neoplasias mamarias se presentaron en la cuarta y quinta glándula mamaria. El 94.7%, de los tumores mamarios presentaron características histológicas malignas, siendo el carcinoma papilar quístico el tipo histopatológico más frecuente (42.8%), seguido por carcinoma tubular (13.9%).

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)0.008
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.415
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.007
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.001

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 head0.017
GPT teacher head0.315
Teacher spread0.298 · 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".

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Citations1
Published2019
Admission routes1
Has abstractyes

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