Nivel de adherencia al tratamiento contra Helicobacter Pylori en menores de 18 años
Bibliographic record
Abstract
La infeccion por Helicobacter Pylori (Hp) tiene una alta prevalencia en paises en vias de desarrollo; en el Peru, se han reportado prevalencias mayores al 50% en biopsias gastricas de pacientes pediatricos. Es importante conocer el nivel de adherencia al tratamiento en la poblacion pediatrica, asi como los efectos adversos mas comunes y las causas de baja adherencia con el fin de preverlas y lograr optimizar el tratamiento, disminuyendo asi las tasas de resistencia antibiotica. El presente estudio tiene como objetivo principal determinar la evidencia existente del nivel de adherencia al tratamiento contra Hp en pacientes menores de 18 anos. Es un estudio de tipo revision sistematica en la cual se realizara la busqueda en Pubmed, Scielo, Lilacs y Cochrane Library, de 1984 en adelante. Dos revisores seleccionaran los resumenes en base a criterios de seleccion para luego revisarlos a texto completo y determinar si cumplen o no. Se aplicara la Escala Newcastle-Ottawa para evaluar la calidad metodologica de los estudios. Si los datos lo permiten, se realizara un metaanalisis de la frecuencia de adherencia. Los datos de adherencia seran presentados con sus intervalos de confianza, las causas de bajo nivel de adherencia seran analizadas de forma descriptiva y los efectos adversos reportados en caso no sean comparables seran descritos cuantitativamente. Se haran analisis de subgrupos segun grupos de edad de los 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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".