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Record W1968870809 · doi:10.1097/mph.0b013e3182707f2e

Impact of Multiple Prenatal Risk Factors on Newborn Iron Status at Delivery

2013· article· en· W1968870809 on OpenAlexaff
Heather M. McLimore, Alyssa Phillips, Sharon E. Blohowiak, Daphne Q.‐D. Pham, Christopher L. Coe, Beth Fischer, Pamela J. Kling

Bibliographic record

VenueJournal of Pediatric Hematology/Oncology · 2013
Typearticle
Languageen
FieldMedicine
TopicIron Metabolism and Disorders
Canadian institutionsHealth Research Foundation
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsMedicineAnemiaIron statusPregnancyUmbilical cordObstetricsFetusCord bloodIron deficiencyGestationPediatricsInternal medicineImmunology

Abstract

fetched live from OpenAlex

BACKGROUND: Maternal anemia and several complications of pregnancy can affect fetal iron acquisition. AIM: Because it is unknown whether the effects of demographic and maternal risk factors (RF) are summative, we examined cord iron status in newborns with multiple RF for acquiring iron deficiency. METHODS: Cord blood indices from healthy control newborns with and without RF for newborn or infant iron deficiency were studied. RESULTS: Newborns with greater RF had poorer erythrocyte and storage iron status. Poorest status was seen if mothers with comorbid obesity and diabetes delivered large-for-gestation newborns. Findings highlight the importance of identifying RF.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.014
GPT teacher head0.297
Teacher spread0.282 · 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 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

Citations45
Published2013
Admission routes1
Has abstractyes

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