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Record W2307594901 · doi:10.1016/s2214-109x(16)00061-9

Rickets and osteomalacia: a call for action to protect immigrants and ethnic risk groups

2016· article· en· W2307594901 on OpenAlexaboutno aff
Wolfgang Högler, Craig F. Munns

Bibliographic record

VenueThe Lancet Global Health · 2016
Typearticle
Languageen
FieldMedicine
TopicVitamin D Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsOsteomalaciaEthnic groupRicketsImmigrationMedicineCall to actionAction (physics)PediatricsPolitical scienceInternal medicineVitamin D and neurologyBusiness

Abstract

fetched live from OpenAlex

The recent mass migration into Europe poses many challenges to society and public health. Prevention programmes within European countries are primarily designed for the risk profile and diversity of their residents, not those of refugees. Refugee children, many of whom arrive unaccompanied,1 are most vulnerable, especially since prevention programmes (vaccinations, vitamin and micronutrient provision, food fortification, routine infant health checks, and medical education) may not be in place in their country of origin, and their parents may be unfamiliar with the need for such programmes.

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)0.023
metaresearch head score (Gemma)0.042
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: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.044
Threshold uncertainty score0.146

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.042
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0060.005
Bibliometrics0.0030.002
Science and technology studies0.0050.007
Scholarly communication0.0090.020
Open science0.0070.015
Research integrity0.0290.037
Insufficient payload (model declined to judge)0.0440.014

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 head0.080
GPT teacher head0.429
Teacher spread0.349 · 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
GenreEditorial

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

Citations22
Published2016
Admission routes1
Has abstractyes

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