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Record W2152924014 · doi:10.21149/spm.v55i5.7247

Low prevalence of iron deficiency anemia between 1981 and 2010 in Chilean women of childbearing age

2013· article· en· W2152924014 on OpenAlexaff
Israel Ríos-Castillo, Alex Brito, Manuel Olivares, Daniel López-de Romaña, Fernando Pizarro

Bibliographic record

VenueSalud Pública de México · 2013
Typearticle
Languageen
FieldMedicine
TopicIron Metabolism and Disorders
Canadian institutionsNutrition International
Fundersnot available
KeywordsMedicineAnemiaTransferrin saturationIron deficiencyMean corpuscular volumeIron-deficiency anemiaHemoglobinFerritinSerum ironPediatricsDemographyInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine the prevalence of anemia and iron status among Chilean women of childbearing age between 1981 and 2010. MATERIALS AND METHODS: Calculation of the prevalence of anemia and iron status was based on multiple cross-sectional iron absorption studies performed in 888 women during this period of time. All studies included measurements of hemoglobin, mean corpuscular volume, zinc protoporphyrin, percentage of transferrin saturation and serum ferritin. Data were grouped by decade (1981-1990, 1991-2000, and 2001-2010). RESULTS: Prevalence of anemia for these decades was 9, 6 and 10%, respectively (p=NS). Iron deficiency anemia was the main cause of anemia in all periods (55, 85 and 75%, respectively; p=NS). A high prevalence of women with normal iron status was observed for all periods (64, 69, and 67, respectively; p=NS). Prevalence of iron deficiency without anemia in 1981-1990, 1991-2000 and 2001-2010 was 7, 20 and 12%, respectively (p<0.05). Finally, prevalence of iron depleted stores was 20, 6 and 10%, respectively (p<0.05). CONCLUSIONS: Prevalence of iron deficiency anemia in Chilean women of childbearing age was mild between 1981 and 2010. More than 60% of childbearing age women presented normal iron status in all periods. However, prevalence of iron depleted stores was moderate during 1981-1990, and was mild during 1991-2000 and 2001-2010.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.009
GPT teacher head0.236
Teacher spread0.227 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations17
Published2013
Admission routes1
Has abstractyes

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