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Record W112036021 · doi:10.1093/pch/20.1.35

Managing type 1 diabetes in school: Recommendations for policy and practice

2015· article· en· W112036021 on OpenAlexaboutno aff
Sarah Lawrence, Elizabeth Cummings, Danièle Pacaud, Andrew Lynk, Daniel L. Metzger

Bibliographic record

VenuePaediatrics & Child Health · 2015
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDiabetes mellitusType 1 diabetesType 2 diabetesInsulinPediatricsFamily medicineNursingEndocrinology

Abstract

fetched live from OpenAlex

Diabetes requiring insulin is increasingly common and likely to impact students in most, if not all, schools. Diabetes and its complications have major personal, social and economic impact, and improved diabetes control reduces the risk of both short- and long-term complications. Evidence shows that more intensive management of diabetes - through frequent blood glucose monitoring, insulin administration with injections and/or insulin pumps, and careful attention to diet and exercise - leads to better control. Since children spend 30 to 35 hours per week at school, effectively managing their diabetes while there is integral to their short- and long-term health. The Canadian Paediatric Society and the Canadian Pediatric Endocrine Group recommend that minimum standards for supervision and care be established across Canada to support children and youth with type 1 diabetes in schools. These recommendations are derived from evidence-based clinical practice guidelines, with input from diabetes care providers from across Canada, and are consistent with the Canadian Diabetes Association's Guidelines for the Care of Students Living with Diabetes at School.

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)0.038
metaresearch head score (Gemma)0.098
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: Empirical · Consensus signal: none
Teacher disagreement score0.124
Threshold uncertainty score0.247

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0380.098
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0060.006
Science and technology studies0.0040.003
Scholarly communication0.0090.009
Open science0.0110.007
Research integrity0.0230.022
Insufficient payload (model declined to judge)0.0260.008

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 head0.063
GPT teacher head0.398
Teacher spread0.335 · 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
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

Citations37
Published2015
Admission routes1
Has abstractyes

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