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Costo-beneficio de un programa preventivo y terapéutico para reducir la deficiencia de hierro en Argentina

2009· article· es· W2149287053 on OpenAlexaboutno aff
Ignacio Drake, Ricardo Bernztein

Bibliographic record

VenueRevista Panamericana de Salud Pública · 2009
Typearticle
Languagees
FieldMedicine
TopicIron Metabolism and Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical scienceMedicinePhilosophy

Abstract

fetched live from OpenAlex

OBJECTIVES: To estimate the direct and indirect cost of iron deficiency (ID) and iron-deficiency anemia (IDA) in Argentina and compare it with the cost of a prevention and treatment program. METHODS: Analysis of a prior scenario to gage the relative cost-benefit of an IDA prevention and treatment program for all low-income children and expectant mothers without social coverage/benefits in Argentina. The economic consequences of ID and IDA were estimated as direct (cost of care for premature birth) and indirect costs (future lost productivity due to poor cognitive development due of children with ID and current reduced productivity of adults with IDA) employing the specific methodology designed by The Micronutrient Initiative (Ottawa, Canada). The interventions were defined according the practical clinical guidelines in use in Argentina and the item costs were taken from Ministry of Health price lists. RESULTS: Each US$ 1.00 invested in an ID and IDA prevention and treatment program, assuming 90% coverage of breastfeeding/pregnant uninsured low-income mothers, would save US$ 33.40 by preventing the economic losses that would otherwise result from these conditions. CONCLUSIONS: DH interventions not only significantly improve the health status of the population, but also offer a considerable savings.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.808
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.010
GPT teacher head0.294
Teacher spread0.285 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2009
Admission routes1
Has abstractyes

Explore more

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