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Record W2581904327 · doi:10.12927/hcq.2017.25019

Pediatric Insulin Pump Therapy: Reflecting on the First 10 Years of a Universal Funding Program in Ontario

2017· article· en· W2581904327 on OpenAlexaffabout
Rayzel Shulman, Fiona A. Miller, Thérèse A. Stukel, Denis Daneman, Astrid Guttmann

Bibliographic record

VenueHealthcare Quarterly · 2017
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Research
Canadian institutionsInstitute of Health EconomicsHospital for Sick Children
Fundersnot available
KeywordsBest practiceMedicineInsulinFamily medicineNursingPolitical scienceInternal medicine

Abstract

fetched live from OpenAlex

We evaluated the universal funding program for pediatric insulin pumps in Ontario by examining the dynamics underlying patterns of pump use and adverse events using population-based health administrative data available at the Institute for Clinical Evaluative Sciences (ICES), supplemented by other data. We found that (1) pump use has increased steadily since 2006 with variation across centres and disparity in use by socioeconomic status; (2) pump discontinuation is uncommon; (3) physicians value pump therapy in numerous ways that provide important insights into patterns of uptake; and (4) the safety profile of pump therapy is, in general, very good; however, individuals of lower socioeconomic status are at an increased risk of acute diabetes complications, most frequently diabetic ketoacidosis. This comprehensive mixed-methods evaluation reveals the need to understand and intervene to reduce social disparities in the use and adverse outcomes of technologies used for diabetes management.

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)0.002
metaresearch head score (Gemma)0.007
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 score0.960
Threshold uncertainty score0.292

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.002
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 head0.112
GPT teacher head0.390
Teacher spread0.278 · 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

Citations7
Published2017
Admission routes2
Has abstractyes

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