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Record W4385563798 · doi:10.1002/ijgo.14948

Identification of women and girls with iron deficiency in the reproductive years

2023· article· en· W4385563798 on OpenAlexaff
Beth MacLean, Michelle Sholzberg, Angela C. Weyand, Jayne Lim, Grace H. Tang, Toby Richards

Bibliographic record

VenueInternational Journal of Gynecology & Obstetrics · 2023
Typearticle
Languageen
FieldMedicine
TopicIron Metabolism and Disorders
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsMedicineIdentification (biology)Iron deficiencyObstetricsInternal medicineAnemia

Abstract

fetched live from OpenAlex

Iron deficiency (ID) is the most common micronutrient deficiency in the world. It is of concern for women and girls of reproductive age as, despite frequent normalization, excessive menstrual blood loss and the iron demands associated with pregnancy increase the risk of developing an ID. Iron deficiency reduces health-related quality of life with symptoms of fatigue, heart palpitations, difficulty concentrating, and poor mental health. When left untreated, ID can escalate to iron deficiency anemia (IDA), where there is an insufficiency of red blood cells, or hemoglobin within these cells, to meet the bodily demands for oxygen transport. Substantial guidance on screening for ID can be found in specific at-risk groups, including pregnant women and patients with renal, cardiac, and inflammatory bowel disease. However, it was unclear whether guidance is available for women of reproductive age. We performed a literature search to explore the current recommendations for screening women of reproductive age for ID. While four manuscripts supportive of screening were found, no official guidance appears to exist regarding screening for this group. In line with the World Health Organization's 10 Principles of Screening, we present a case for ID screening in women and girls of reproductive age.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.107
Threshold uncertainty score0.390

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.011
GPT teacher head0.280
Teacher spread0.269 · 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

Citations42
Published2023
Admission routes1
Has abstractyes

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