Resección endoscópica de leiomiosarcoma traqueal en un perro
Bibliographic record
Abstract
En el presente informe se describe el caso de un paciente canino de raza Labrador Retriever de 12 años que presentaba disnea severa y cianosis no responsiva a tratamiento médico con presencia de tejido radiodenso evidente por rayos X en el tercio proximal de la tráquea compatible con neoformación, por lo cual se indicó traqueoscopia con fines diagnósticos y terapéuticos. Durante la traqueoscopia se encontró una masa pediculada ocupando el 80% de la luz traqueal. Se realizó resección completa desde el pedículo mediante pinza de polipectomía, corte y coagulación monopolar y se realizó el rescate de la masa con cesta endoscópica para recuperación de pólipos. El análisis histopatológico de la estructura reveló un leiomiosarcoma, de presentación inusual en tráquea. La resección endoscópica de leiomiosarcoma traqueal no ha sido reportada en el país y hay escasos reportes a nivel mundial, por lo que la difusión del caso resulta interesante en el medio académico, ya que se está mostrando la posibilidad de intervenciones en tráquea por medio de métodos quirúrgicos no invasivos y se reporta una localización poco común para un leiomiosarcoma, logrando así proponer este trabajo como referencia para futuros profesionales que manejen casos con características similares.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".