Characterization of the Most Common Diseases in Children Under 5 Years (Pneumonia and Malnutrition): A Correlation Analysis.
Bibliographic record
Abstract
INTRODUCTION: In Latin America the nutritional deficit is the most common problem affecting children under 5 years old, which is not an strange issue to Cartagena’ s population, this type of phenomenon is due to lack of nutritional education or food security that leads to appearance of infectious diseases such as pneumonia becoming a public health problem.OBJECTIVE: To correlate the clinical features of children with malnutrition, pneumonia and malnutrition, and pneumonia from Cartagena de Indias. 2010 to 2012.METHODOLOGY: Retrospective cross-sectional descriptive-correlational study. 220 medical records from three groups were analyzed, including 98 children with malnutrition, 100 children with pneumonia and 22 children with malnutrition and pneumonia simultaneously medically attended during 2012-2014. Logistic regression was performed applying Pearson and Durbin Watson calculations with the software SPSS 20.0 ®.RESULTS: Male children have a higher prevalence of malnutrition and pneumonia simultaneously and separately by 63%. The age with more pneumonia and malnutrition cases is in children under 2 years old from stratum I, likewise weight and respiratory problems correlate as common clinical features.CONCLUSION: It is corroborated the existence of possible signs and symptoms common to pneumonia and malnutrition, in addition the design of dynamic programs that keep into account the environmental conditions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".