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Record W4213255143 · doi:10.23880/phoa-16000138

Maivel Emile Soby Gerges, Ghada Essam Aldin, Diaa Marzouk Abdel Hamid and Mohamed Farouk Allam*

2019· article· en· W4213255143 on OpenAlexaboutno aff
Mohamed Farouk Allam

Bibliographic record

VenuePublic Health Open Access · 2019
Typearticle
Languageen
FieldMedicine
TopicVitamin D Research Studies
Canadian institutionsnot available
Fundersnot available
Keywordsvitamin D deficiencyMedicineOsteomalaciaOsteoporosisVitamin D and neurologyPediatricsPopulationPolycystic ovaryOsteopeniaInternal medicineEnvironmental healthObesityBone mineral

Abstract

fetched live from OpenAlex

Vitamin D deficiency is a common public-health problem. Deficiency is more common in women than men, and the childbearing period is known to represent a particularly high-risk situation. High rates of poor vitamin D status are found among women during the childbearing period throughout the world. Women at reproductive age are a group that can be susceptible at earlier age for vitamin D deficiency and its complications as increase the risk of osteopenia, osteoporosis, muscle weakness, osteomalacia and pathological fractures and can worsen other chronic conditions, such as the polycystic ovary syndrome; it is also a risk factor for cardiovascular diseases, metabolic syndrome, some types of cancers and some autoimmune diseases. Several studies have identified a surprisingly high prevalence of vitamin D deficiency in all age groups such as in Europe after 14 population study, United States, Canada and Australia. Despite reported prevalence of vitamin D deficiency and insufficiency depend on the cut-off values used that vary between studies, an estimated 1 billion people worldwide have vitamin D deficiency or insufficiency or hypovitaminosis D. National surveys should be conducted in every country to determine normal levels of vitamin D in that country and the need for national screening programs for vitamin D deficiency.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.258
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0020.004
Research integrity0.0000.001
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 head0.161
GPT teacher head0.476
Teacher spread0.315 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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