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Record W4213219391 · doi:10.35753/rchsi.v5i4.236

Câncer da Vesícula Biliar - Relato de Casos

2022· article· pt· W4213219391 on OpenAlexaff
Marcos Dantas Moraes Freire, Luís Fernando Pinto Johnson, Mateus Dantas Moraes Freire, Maria Luiza Silva Casé, Lucas Torres Pires, André Luís de Godoy, André Ney Menezes Freire

Bibliographic record

VenueRevista Científica Hospital Santa Izabel · 2022
Typearticle
Languagept
FieldMedicine
TopicCholangiocarcinoma and Gallbladder Cancer Studies
Canadian institutionsAlberta University of the Arts
Fundersnot available
KeywordsMedicineGynecologyGastroenterology

Abstract

fetched live from OpenAlex

O câncer da vesícula biliar é uma doença que representa um grande desafio para médicos clínicos e cirurgiões, por possuir características especiais: alta capacidade de disseminação, invasão por contiguidade e continuidade, crescimento rápido e agressivo, além de apresentação clínica insidiosa, geralmente apresentando sintomas quando o quadro já está avançado. Apresentamos dois casos com desfechos completamente diferentes: um do Hospital Santa Izabel e outro do hospital especializado em câncer A.C. Camargo de São Paulo. Discutimos ainda o tratamento atual e as possíveis intervenções que podem ser traçadas no futuro para o aprimoramento no diagnóstico e na terapêutica dessa grave doença.

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)0.004
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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.018
GPT teacher head0.271
Teacher spread0.253 · 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
Published2022
Admission routes1
Has abstractyes

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