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Record W2065753733 · doi:10.1182/blood-2004-12-4782

The influence of high-altitude living on body iron

2005· article· en· W2065753733 on OpenAlexaff
James D. Cook, Erick Boy, C.W. Flowers, Maria del Carmen Daroca

Bibliographic record

VenueBlood · 2005
Typearticle
Languageen
FieldMedicine
TopicIron Metabolism and Disorders
Canadian institutionsNutrition International
Fundersnot available
KeywordsIron statusSoluble transferrin receptorSerum ferritinFerritinPopulationIron deficiencyMedicineEffects of high altitude on humansDemographyAltitude (triangle)Transferrin receptorBody weightPhysiologyAnimal scienceTransferrinBiologyAnemiaEndocrinologyInternal medicineEnvironmental healthMathematics

Abstract

fetched live from OpenAlex

The quantitative assessment of body iron based on measurements of the serum ferritin and transferrin receptor was used to examine iron status in 800 Bolivian mothers and one of their children younger than 5 years. The survey included populations living at altitudes between 156 to 3750 m. Body iron stores in the mothers averaged 3.88 +/- 4.31 mg/kg (mean +/- 1 SD) and 1.72 +/- 4.53 mg/kg in children. No consistent effect of altitude on body iron was detected in children but body iron stores of 2.77 +/- 0.70 mg/kg (mean +/- 2 standard error [SE]) in women living above 3000 m was reduced by one-third compared with women living at lower altitudes (P < .001). One half of the children younger than 2 years were iron deficient, but iron stores then increased linearly to approach values in their mothers by 4 years of age. When body iron in mothers was compared with that of their children, a striking correlation was observed over the entire spectrum of maternal iron status (r = 0.61, P < .001). This finding could provide the strongest evidence to date of the importance of dietary iron as a determinant of iron status in vulnerable segments of a population.

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 score0.531
Threshold uncertainty score0.167

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 head0.005
GPT teacher head0.237
Teacher spread0.232 · 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

Citations103
Published2005
Admission routes1
Has abstractyes

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