Patient Values and Preferences Regarding Continuous Subcutaneous Insulin Infusion and Artificial Pancreas in Adults with Type 1 Diabetes: A Systematic Review of Quantitative and Qualitative Data
Bibliographic record
Abstract
OBJECTIVE: We produced, through a systematic review of quantitative and qualitative evidence, a synthesis of the issues of importance (values and preferences) to adult patients with type 1 diabetes regarding treatment with automated insulin delivery systems. METHODS: We searched MEDLINE, CINAHL, EMBASE, and PsycINFO from the inception of each database through September 2018. We included studies examining patient values and preferences for outcomes related to continuous subcutaneous insulin infusion or artificial pancreas treatment. We compiled structured summaries of the results and assessed the relative importance of each outcome. GRADE (Grading of Recommendations, Assessment Development, and Evaluation) and CERQual (Confidence in Evidence from Reviews of Qualitative research) approaches provided the structure for the evaluation of the quality of evidence and confidence in the findings. A mixed-methods result-based convergent design provided the structure for integration and presentation of results. RESULTS: We reviewed 1665 unique citations; 19 studies (8 quantitative and 11 qualitative) proved eligible. Glycemic control is the key attribute that drives patients' preference. Reduction of glycemic variability and decreased incidence of hypoglycemia and chronic complications proved of intermediate importance and were ranked similarly to components of treatment burden, including the size and appearance of devices, cost, ease of use, and the embarrassment of public use. CONCLUSIONS: Clinician guidance may play a crucial role in determining patient values and preferences (for instance, patients' priority in glucose control rather than avoiding diabetic complications). Our results provide guidance for clinicians in discussing preferred insulin delivery systems with patients with type 1 diabetes.
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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 | 0.074 | 0.250 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".