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Alterações Genéticas em Câncer de Cabeça e Pescoço

2009· article· pt· W3135296007 on OpenAlexaff
Jucimara Colombo, Paula Rahal

Bibliographic record

VenueRevista Brasileira de Cancerologia · 2009
Typearticle
Languagept
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsInterface Biologics (Canada)
Fundersnot available
KeywordsMedicineGynecology

Abstract

fetched live from OpenAlex

O carcinoma de células escamosas de cabeça e pescoço (HNSCC) constitui o quinto tipo de câncer mais comum mundialmente, com uma incidência anual global de 780.000 novos casos. Os sítios comuns incluem cavidade oral, orofaringe, hipofaringe, nasofaringe, cavidade nasal, seios paranasais, laringe e glândulas salivares. O consumo de tabaco e/ou álcool são os principais fatores de risco envolvidos no desenvolvimento do HNSCC. Apesar dos recentes avanços no tratamento, o índice de sobrevivência dos pacientes com HNSCC tem permanecido em 40%. A recidiva locorregional e a metástase após terapia convencional parecem ser os principais fatores que contribuem para a sobrevivência reduzida dos pacientes. O desenvolvimento do câncer de cabeça e pescoço é um processo multipasso acompanhado por mudanças genéticas e epigenéticas, incluindo perda de heterozigozidade, inativação gênica por metilação e amplificação gênica. Diferentes estudos têm revelado numerosas alterações moleculares em HNSCC, incluindo ativação de oncogenes, tais como: EGFR, ciclina D1 e COX-2; inativação de genes supressores tumorais, tais como: TP53, p16, p27 e WAF1/C1P1; e expressão de fatores angiogênicos e metastáticos; além dos polimorfismos genéticos de enzimas metabólicas. Esta revisão apresenta informações atuais sobre as principais alterações genéticas envolvidas no desenvolvimento do câncer de cabeça e pescoço, as quais apresentam potencial valor prognóstico, e discute alguns fatores que contribuem para a controvérsia a respeito de sua importância prognóstica.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.036
GPT teacher head0.336
Teacher spread0.300 · 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 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

Citations22
Published2009
Admission routes1
Has abstractyes

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