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Record W6977924526 · doi:10.71918/229

Detección de marcadores moleculares mediante biopsia líquida en tumores cerebrales pediátricos y del adolescente.

2022· other· es· W6977924526 on OpenAlexaboutno aff

Bibliographic record

VenueCEU Repositorio Institucional (Fundación Universitaria San Pablo CEU) · 2022
Typeother
Languagees
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomic variations and chromosomal abnormalities
Canadian institutionsnot available
Fundersnot available
KeywordsBiopsyTumor cellsLabrador Retriever

Abstract

fetched live from OpenAlex

Los tumores del sistema nervioso central (SNC) son la principal neoplasia sólida y la causa más frecuente de mortalidad por cáncer en la edad pediátrica. Debido a su localización y naturaleza infiltrativa, algunas resecciones o biopsias tumorales no resultan factibles o acarrean especial morbilidad. En estos casos, el desarrollo de técnicas mínimamente invasivas que permitan obtener información derivada del tumor, útil en el proceso diagnóstico, pronóstico, terapéutico y de seguimiento a largo plazo, cobran especial relevancia. La innovadora técnica denominada biopsia líquida, mediante la cual se pretende analizar material genético derivado de las células tumorales aislado en fluídos biológicos, se encuentra en pleno desarrollo, siendo éste mucho más amplio en adultos que en niños. En la presente tesis doctoral, se ha comparado la detección de la mutación diana V600E del gen BRAF mediante PCR digital en ADN circulante obtenido de una cohorte de 29 pacientes pediátricos con tumores cerebrales, en tres fuentes diferentes de biopsia líquida: suero, plasma y líquido cefalorraquídeo. Se demuestra que el ADN circulante aislado a partir de suero y plasma, podría analizarse con éxito para obtener información sobre la genética tumoral, la cual guiaría el manejo clínico de estos pacientes.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.842
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.005
GPT teacher head0.202
Teacher spread0.197 · 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 teacher head, not a consensus.

Study designNot applicable
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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