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Record W2897644637

Adenocarcinoma das glândulas apócrinas dos sacos anais em cães

2018· dissertation· pt· W2897644637 on OpenAlexaboutno aff
Madalena Nogueira Dias Freire

Bibliographic record

VenueRepositório Científico Lusófona (Grupo Lusófona) · 2018
Typedissertation
Languagept
FieldMedicine
TopicSalivary Gland Tumors Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesMedicineArt
DOInot available

Abstract

fetched live from OpenAlex

O adenocarcinoma das glândulas apócrinas dos sacos anais em cães é uma neoplasia de carácter maligno, com taxas de metastização de 36 até 96%. O objectivo deste estudo foi comparar os métodos de condução de casos de adenocarcinoma das glândulas apócrinas dos sacos anais num hospital de referência, em cirurgia de tecidos moles, no Reino Unido, com a metodologia descrita na literatura de evidência médica atual. O presente estudo incluiu a documentação de 4 casos de cães presentes à consulta de especialidade cirúrgica com queixa compatível com adenocarcinoma das glândulas apócrinas dos sacos anais no hospital veterinário Rosemary Lodge. Dois dos pacientes eram da raça Labrador Retriever, que pode ter sido devido à popularidade da raça em Inglaterra. A maioria dos sinais clínicos não eram específicos nem sistémicos, no entanto todos os pacientes apresentaram hipercalcémia nas análises clínicas, o que está descrito como um síndrome paraneoplásico neste tipo de tumor. Neste estudo constatou-se que os casos de AGASA foram conduzidos apenas com uma abordagem cirúrgica, não tendo sido submetidos a tratamento adjuvante segundo as guidelines recomendadas pelos especialistas em Oncologia. Conclui-se que é necessário consciencializar a comunidade veterinária, de forma a que os casos oncológicos sejam uniformemente referenciados para tratamentos adjuvantes ou coadjuvantes como descritos na literatura médica.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.018
GPT teacher head0.304
Teacher spread0.286 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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

Citations0
Published2018
Admission routes1
Has abstractyes

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