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Record W2809044660 · doi:10.2337/db18-2272-pub

Predictors of Preferences for Use of MK-1293 Pens among Patients with Diabetes—Findings from a Multinational Study in the U.S., Canada, and U.K.

2018· article· en· W2809044660 on OpenAlexaboutno aff
Berhanu Alemayehu, Allison Martin Nguyen, Marco DiBonaventura, Bijal Shah‐Manek, Chitra Karki, Michael F. Crutchlow

Bibliographic record

VenueDiabetes · 2018
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical studies and practices
Canadian institutionsnot available
Fundersnot available
KeywordsInsulin glargineInsulin penMedicineLogistic regressionDemographicsType 2 diabetesPreferenceFamily medicineDiabetes mellitusDemographyInsulinInternal medicinePsychologyEndocrinologyMathematics

Abstract

fetched live from OpenAlex

This study examines the predictors of preferences of pen devices used with originator insulin glargine and MK-1293, a recently approved biosimilar insulin glargine (referred to as “Pen X”), among patients with diabetes. Adult patients with type 1/type 2 diabetes were recruited from commercial panels in the U.S., Canada, and the UK for one hour study visits. Respondents rated several attributes of each pen along with overall preferences for each of the two insulin pens. Non-inferiority analyses were conducted to compare ratings of the originator insulin glargine pen with Pen X. Logistic regression modeling was conducted to identify predictors of overall preference. A total of N=177 patients completed the study: mean age of 53.2 years, 56% males, with 73% type 2 diabetes. Overall, 63.2% preferred Pen X, 22.3% preferred originator insulin glargine pen and 14.5% had no preference. Except for employment, demographics were not significantly associated with overall preference, however the ease of pushing the injection button, instruction booklet and overall quality of the pen were predictive of an overall preference of Pen X (Table 1).The current study suggests that >60% of patients preferred Pen X and that overall, functional use and individual pen characteristics were the strongest predictors of preference, after adjusting for other pen characteristics. Disclosure B. Alemayehu: Employee; Self; Merck & Co., Inc. A.M. Nguyen: Employee; Self; Merck & Co., Inc. M. DiBonaventura: Employee; Self; Pfizer Inc.. Consultant; Self; Merck & Co., Inc.. B. Shah-Manek: None. C. Karki: None. M. Crutchlow: Employee; Self; Merck & Co., Inc.. Consultant; Spouse/Partner; Novo Nordisk Inc., Zealand Pharma A/S, XOMA Corporation.

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 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)0.000
metaresearch head score (Gemma)0.000
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 score0.079
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.042
GPT teacher head0.294
Teacher spread0.253 · 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 teacher head, 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

Citations0
Published2018
Admission routes1
Has abstractyes

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