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Predictors of glucose control in children and adolescents with type 1 diabetes mellitus

2005· article· en· W2137520506 on OpenAlexaff
Stacey Urbach, Stephen LaFranchi, Lori Lambert, Jodi Lapidus, Denis Daneman, Thomas Becker

Bibliographic record

VenuePediatric Diabetes · 2005
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Research
Canadian institutionsHospital for Sick Children
FundersOregon Health and Science University
KeywordsMedicineType 1 diabetesDiabetes mellitusPediatricsMetabolic control analysisContinuous glucose monitoringInternal medicineEndocrinology

Abstract

fetched live from OpenAlex

AIMS: To evaluate the glucose control [(as measured by hemoglobin A1c (HbA1c)], the factors associated with glycemic control, and possible explanations for these associations in a sample of children and adolescents with type 1 diabetes. METHODS: Data were collected on 155 children and adolescents, with type 1 diabetes mellitus, attending a multidisciplinary diabetes clinic in Portland, OR. Patients' hospital charts were reviewed to determine demographic factors, disease-related characteristics, and HbA1c level. RESULTS: Mean percent HbA1c was 9.3. Adolescents between the ages of 14 and 18 yr were in poorer metabolic control (adjusted mean percent HbA1c 0.56 higher than children 2-8 yr). Children who attended the clinic three to four times in the previous year were in better control (adjusted mean percent HbA1c 0.46 lower than those who visited two or fewer times and 1.11 lower than those who attended five or more times). Children with married parents were in better glycemic control than those of single, separated, or divorced parents (adjusted mean percent HbA1c 0.47 lower for children of married parents). This effect appeared to be mediated, in part, by the number of glucose checks performed per day. CONCLUSIONS: This study suggests that adolescents should be targeted for improved metabolic control. Diabetes team members need to be aware of changing family situations and provide extra support during stressful times. Regular clinic attendance is an important component of intensive diabetes management. Strategies must be developed to improve accessibility to the clinic and to identify patients who frequently miss appointments.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.004
GPT teacher head0.216
Teacher spread0.212 · 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

Citations90
Published2005
Admission routes1
Has abstractyes

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