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Record W4385843463 · doi:10.15273/hpj.v3i2.11552

The Importance of Iron Supplementation in Pregnant Women

2023· article· en· W4385843463 on OpenAlexaff
Abdullah Chanzu, Molly Wells, Natasha Vitkin, Sarah Nersesian

Bibliographic record

VenueHealthy Populations Journal · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicHealth Education and Validation
Canadian institutionsSimon Fraser UniversityDalhousie University
Fundersnot available
KeywordsInfographicContext (archaeology)RegimenPictogramAnemiaIron supplementationMedicineOrder (exchange)Iron deficiencyComputer scienceInternal medicineLinguisticsBusinessGeographyData mining

Abstract

fetched live from OpenAlex

Iron supplementation is an important treatment for pregnant people with iron deficiency anemia. For this reason, we designed and created an evidence-based infographic with accessible fonts, pictograms, and language. This tool serves as a framework for the potential of visual communication tools in the context of improving medication and treatment adherence. Various sources were utilized to derive information on a suggested iron intake regimen; however, we encourage individuals to consult with their primary care providers in order to establish the ideal regimen for them, adjusted to unique individual factors.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.044
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.044
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.199
GPT teacher head0.508
Teacher spread0.309 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations0
Published2023
Admission routes1
Has abstractyes

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