Bibliographic record
Abstract
Peru is the South American country that suffers anaemia the most (matching only Guyana) according to the WHO. It affects more than 50% of preschool children, 42% of pregnant women and 40% of non-pregnant women of reproductive age. These prevalence levels put Peru in a similar situation to most African countries. In spite of the important role of anaemia in Peruvian society, the magnitude of the problem has not been acknowledged in its consequences and costs for the country. Furthermore, the Peruvian state has not developed a systematic policy for fighting anaemia. The aim of this study is to identify and estimate the economic costs for the Peruvian state and economy caused by the current prevalence of iron-deficiency anaemia among adults; to estimate the future economic costs for the Peruvian economy of the current prevalence of anaemia among children and to estimate the costs incurred by the state from the anaemia-related care provided, as well as that related to consequent health problems. Furthermore, the study also undertakes an estimation of the costs that the Peruvian state would incurred in order to prevent anaemia among children and pregnant women. The aim of this is to show the importance and implications of the problem and the possible savings and benefits of a stronger, more systematic and more effective policy for fighting anaemia.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".