Detección de marcadores moleculares mediante biopsia líquida en tumores cerebrales pediátricos y del adolescente.
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".