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Record W3042893246 · doi:10.20517/2347-9264.2020.99

Equality in cleft and craniofacial care

2020· article· en· W3042893246 on OpenAlexaff
Nicholas Sharratt, Jean Calleja‐Agius, Gareth Davies, Felicity V. Mehendale, Peter Hagell, Martin Persson

Bibliographic record

VenuePlastic and Aesthetic Research · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCleft Lip and Palate Research
Canadian institutionsCentre for Global Health Research
Fundersnot available
KeywordsInequalityEuropean unionAction (physics)Health careMedicineSocioeconomic statusCraniofacialSocial inequalityPublic relationsPolitical scienceLawEnvironmental healthPopulationBusinessPsychiatry

Abstract

fetched live from OpenAlex

This review examines the issue of equality of care amongst those with cleft lip and/or palate in the European Union (EU) and beyond. Issues of equality both between and within national populations are considered, and it is argued that those from countries with smaller healthcare expenditure and who are from marginalised groups are at the greatest risk of, and affected most severely by, healthcare inequalities. The socioeconomic impact of inequality is also discussed. Having reviewed these topics, the goals and activities of the European Cleft and Craniofacial Initiative for Equality in Care Action, formed pursuant to an award from the EU’s European Cooperation in Science and Technology, are introduced. Constituted of an open network of clinicians and researchers committed to exploring and reducing such inequalities, the ongoing Action is formed of multiple working groups examining these issues within the EU and has organised training schools, conferences and short-term scientific missions concerned with these issues. These activities are discussed along with the future directions of the Action, the impact it has had to date and the benefits of the European Cooperation in Science and Technology award.

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)0.000
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 score0.204
Threshold uncertainty score0.327

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.065
GPT teacher head0.359
Teacher spread0.294 · 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

Citations6
Published2020
Admission routes1
Has abstractyes

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