MétaCan
Menu
Back to cohort
Record W2149067383 · doi:10.1177/193229681200600118

Analysis of Use of an Automated Bolus Calculator Reduces Fear of Hypoglycemia and Improves Confidence in Dosage Accuracy in Type 1 Diabetes Mellitus Patients Treated with Multiple Daily Insulin Injections

2012· letter· en· W2149067383 on OpenAlex
Frank Schwartz, Aili Guo, Cynthia R. Marling, Jay H. Shubrook

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

VenueJournal of Diabetes Science and Technology · 2012
Typeletter
Languageen
FieldMedicine
TopicDiabetes Management and Research
Canadian institutionsHeritage College
Fundersnot available
KeywordsHypoglycemiaMedicineDiabetes mellitusType 1 diabetesBolus (digestion)InsulinIntensive care medicineCalculatorInsulin penType 2 Diabetes MellitusLimitingInternal medicineEndocrinologyComputer science

Abstract

fetched live from OpenAlex

In this issue of Journal of Diabetes Science and Technology, Barnard and colleagues evaluate the use of the ACCU-CHEK® Aviva Expert blood glucose meter/bolus advisor system in patients with type 1 diabetes mellitus. Hypoglycemia is a major limiting factor to intensive glucose control, and fear of hypoglycemia, especially in those who have experienced severe reactions, is a major barrier. The bolus advisor improved overall glucose control and increased adherence by overcoming the patients' fear of hypoglycemia, giving them more confidence to give adequate doses of insulin to control hyperglycemia. In this review, we discuss other human factors that become barriers to intensive control, which can benefit from new technologies, including numeracy literacy, information overload, time required for diabetes self-care, and device incompatibility.

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)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.016
Threshold uncertainty score0.703

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0060.006
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.283
Teacher spread0.267 · 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