Android-Based Mobile Application for the Control of Anemia in Children Aged 6 to 23 Months Old in the District of Comas – Lima
Bibliographic record
Abstract
One of the main health problems facing the country is iron deficiency anemia. This condition reduces the level of hemoglobin present in the blood, which produces symptoms that affect the sufferer's daily activities. According to the INEI (National Institute of Statistics and Informatics), the urban area has 36.7% of anemia, and Comas is the second district of the capital (Lima) with the second highest rate of anemia with 43.4%. This article proposes a technological solution that allows the control and prevention of iron deficiency anemia in children from 6 to 23 months old in Comas through the follow-up of the screenings performed and by improving eating habits. The result obtained was an Android-based mobile application with three navigation options. The Children option allows the registration of each screening performed and notifies the level of anemia present in the child, the Home option displays information about this condition, and the Kitchen Recipes option is responsible for enriching the child's diet by presenting a highly nutritious recipe book, categorized according to age range. It also obtained a 90% positive acceptance rate from the sample population identified as users and patients, and a daily screening was recorded. This result can be applied to any coastal area of the country and, therefore, contributes to improving the quality of life of children by reducing anemia rates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.007 | 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".