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Record W2532947244 · doi:10.1111/pedi.12427

Contribution of SWEET to improve paediatric diabetes care in developing countries

2016· review· en· W2532947244 on OpenAlex
Danièle Pacaud, Jean-François Lemay, Erick Richmond, Stéphane Besançon, Dhruvi Hasnani, Sujata Jali, Carmen Mazza

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePediatric Diabetes · 2016
Typereview
Languageen
FieldMedicine
TopicDiabetes Management and Research
Canadian institutionsUniversity of CalgaryAlberta Children's Hospital
FundersMedtronic FoundationSanofiMedtronic EuropeBoehringer IngelheimAstraZeneca
KeywordsMedicineDeveloping countryDiabetes mellitusPerspective (graphical)PopulationFamily medicineGerontologyEnvironmental healthEconomic growth

Abstract

fetched live from OpenAlex

Diabetes affects many children living in developing countries. Through an informal survey, five SWEET (Better control in Pediatric and Adolescent diabeteS: Working to crEate CEnTers of Reference) centers from developing countries (Mali, Costa Rica, Argentina and two from India) share their perspective on caring for children with diabetes. Each center provides a description of the population of children with diabetes they serve, the organization of care, and the challenges encountered on a daily basis in the provision of this care. In the second part, we summarize the anticipated benefits and challenges associated with participation in SWEET. This resulting article is a testimony of the reality of managing diabetes by dynamic teams striving to achieve recommended standards of care for pediatric diabetes in an environment with limited resources.

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.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.981
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.015
GPT teacher head0.309
Teacher spread0.293 · 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