Análisis epidemiológico de la presentación de Ehrlichia sp.\nen caninos de Florencia Caquetá, Colombia
Bibliographic record
Abstract
"Se realizó un estudio epidemiológico sobre aspectos relacionados con la Ehrlichiosis en caninos con el objeto de conocer la frecuencia y distribución de esta afección en la ciudad de Florencia (Caquetá, C olombia) y los principales factores causales asociados. Se analizaron 98 pacie ntes que ingresaron a consulta en una clínica veterinaria con síntomas co mpatibles con la patología, teniendo en cuenta la historia clínica, encuesta ep idemiológica y prueba de laboratorio (visualización directa por frotis sangu íneo mediante coloración de Giemsa); para determinar el nivel de asociación de los factores con la enfermedad se calculó el ODSS RATIO (OR). Se encont ró una frecuencia de la enfermedad del 22.4% y se definieron factores predi sponentes (sexo, edad y raza), factores favorecedores (alimentación mixta, hábitat, clima y control deficiente de vectores), factores reforzadores (dif ícil diagnóstico, desnutrición, re-infestación de garrapatas, contacto con animales infectados) y factores precipitantes (contacto con vectores infectados). D entro de los factores influyentes del agente se identificaron el uso vect ores y la naturaleza del patógeno. Como factores de riesgo se determinaron: hábitat inadecuado (OR = 45.0), control deficiente de garrapatas (OR = 39.8) , edad adulto (OR = 33.7), raza pura Labrador (OR = 20.1 y sexo macho (OR = 2. 5). Estos resultados permitirán plantear estrategias eficientes de preve nción, manejo y control de la enfermedad."
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".