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Record W2050819784 · doi:10.1002/pdi.1634

Detecting diabetes complications in children

2011· article· en· W2050819784 on OpenAlexaboutno aff
Rita Bertalan, J W Gregory

Bibliographic record

VenuePractical Diabetes · 2011
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDiabetes mellitusExcellenceLife expectancyType 1 diabetesPediatricsIntensive care medicineQuality of life (healthcare)Family medicineEnvironmental healthNursingPopulationEndocrinology

Abstract

fetched live from OpenAlex

Abstract Clinical symptoms of diabetes‐related complications are very rare in children and adolescents with type 1 diabetes (T1D). Screening for complications aims to detect their presence shortly after development but before they cause clinically significant symptoms. Early detection of complications, alongside efforts to improve glycaemic control, can slow the progression of microvascular complications with consequently improved quality of life and life expectancy. An ideal screening programme should be evidence based and should include the majority of clinically important complications and associated diseases. Such programmes have been formulated by multidisciplinary bodies representing a number of specialist diabetes societies worldwide. The purpose of this review is to highlight the importance of screening for diabetes complications and comorbidities in T1D in childhood and to review and compare the latest guidelines of the International Society for Pediatric and Adolescent Diabetes, American Diabetes Association, Canadian Diabetes Association, Australian Government National Health and Medical Research Council, and the UK National Institute for Health and Clinical Excellence. Copyright © 2011 John Wiley & Sons.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.070
GPT teacher head0.341
Teacher spread0.271 · 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 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

Citations2
Published2011
Admission routes1
Has abstractyes

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