TL 745 DETECCIÓN DE FIBROSIS HEPÁTICA POR MEDIO DEL MÉTODO FIB-4 Y SU RELACIÓN CON EVENTOS CARDIOVASCULARES EN UNA COHORTE DE ATENCIÓN PRIMARIA DE SALUD
Bibliographic record
Abstract
Introduccin: En el contexto de la enfermedad por hgado graso no alcohlico (HGNA), se ha descrito una asociacin entre riesgo cardiovascular y la presencia de fibrosis heptica. FIB-4 es un ndice no invasivo para estimar la presencia de fibrosis heptica. Objetivo: Evaluar la relacin entre FIB 4 y eventos cardiovasculares (ECV) en una cohorte de pacientes con enfermedades crnicas. Mtodos: Estudio observacional, de cohorte retrospectivo (2009-2019) de pacientes seguidos en Consultorio Miraflores, Temuco, regin de la Araucana, con datos que permitan calcular FIB-4. Anlisis de ECV incluyendo Infarto miocrdico (IAM) y accidente cere-brovascular (ACV) en el perodo de anlisis del estudio. Resultados: se incluyeron 1.532 pacientes; [65% eran hombres; edad media de 77 aos (64-84)]. El 91,4% tena dislipidemia, 73,8% hipertensin y 34,5% diabetes mellitus. El ndice FIB-4 basal fue de 1,29 (0,91-1,74) y el final de 1,59 (1,12-2,2). La progresin de la fibrosis medida con FIB-4 se asoci en forma independiente con un aumento de riesgo de IAM (47%, IC al 95% 1,06-2,05; p = 0,021) pero no de ACV. Conclusin: La progresin de la fibrosis heptica, estimada por FIB-4, se asoci a mayor riesgo de IAM en la cohorte en estudio. El empleo de FIB-4 puede ser de utilidad para una bsqueda ms activa y sistemtica de fibrosis heptica en pacientes con enfermedades crnicas. Ello contribuira tanto a la pesquisa de enfermedad heptica como a la estimacin del riesgo de ECV. Fondecyt #1191145 a M.A., #1191183 a F.B.,
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".