Risking Health to Avoid Injections
Bibliographic record
Abstract
Improved glycemic control reduces the risk of long-term diabetes complications (1–3). However, subcutaneous insulin injections represent a barrier to achieving “optimal” blood glucose levels, particularly among type 2 diabetic patients (4). Indeed, some patients even delay initiation of therapy to avoid injections (5). This study used conjoint analysis to quantify the relative importance that Canadian patients with type 2 diabetes place on short-term treatment outcomes and on the frequency of insulin injections. A total of 1,886 patients enrolled in a Canadian consumer panel ( n = 70,000) were mailed a questionnaire. Study entry criteria were age ≥18 years and self-reported type 2 diabetes. The choice format conjoint questionnaire was designed to reveal the relative importance patients place on various health outcomes and treatment attributes associated with insulin therapy. This format offers advantages over other methods of quantifying health care preferences (6–11). The questions comprised 12 hypothetical treatment choices, including varying numbers of daily insulin injections using an insulin pen (one to three injections), levels of glucose control (optimal, suboptimal, and poor as fasting plasma glucose levels of 4–7, 7.1–10, and >10 mmol/l, respectively), HbA1c (A1C) levels 8.4%), and numbers of mild-to-moderate hypoglycemic events per month ( 2). Insulin pens were chosen over other methods of subcutaneous insulin delivery because they are the predominant method used in Canada (12). One alternative in each question was a constant reference condition. …
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".