Flexible insulin dosing improves health-related quality-of-life (HRQoL): a time trade-off survey
Bibliographic record
Abstract
PURPOSE: People with insulin-treated diabetes often face strict regimens with inflexible dose timing, frequent injections, and frequent self-measured blood glucose (SMBG) testing. The objective of this study was to estimate the health-related quality-of-life (HRQoL) impact of these aspects using time trade-off (TTO) methods. METHODS: HRQoL was examined via a TTO survey in the UK, Canada, and Sweden with separate analyses of 2465 respondents from the general population, 274 people with type 1 diabetes, and 417 people with type 2 diabetes. Respondents evaluated health states with diabetes, SMBG testing, and basal injections that were once-daily time flexible, once-daily at a fixed time, and twice-daily at a fixed time in a basal or basal-bolus regimen. RESULTS: Time-flexible basal injections were associated with 0.016 and 0.013 higher utility vs a fixed time of injection for basal-only and basal-bolus regimens, respectively, as evaluated by the general population. The diabetes respondents confirmed the basal-only results with 0.015 higher utility, but the difference in utility was non-significant for basal-bolus. Once-daily injections had higher utility compared with twice-daily injections for basal (0.039 and 0.042) and basal-bolus (0.022 and 0.021) regimens, as evaluated by the general population and people with diabetes, respectively. Increased frequency of SMBG negatively affected health utility. LIMITATIONS: This study has the limitation that it measures hypothetical health states rather than the HRQoL of people with these health states; furthermore, it could be suggested that the web-based nature of this survey is biased towards literate respondents with internet access and IT competence. CONCLUSIONS: Flexible dosing and fewer injections have a positive HRQoL impact, which potentially may enhance therapy adherence and could contribute to improved long-term outcomes. The impact of flexibility is greater in people treated with basal-only insulin regimens, and diminishes if bolus injections are part of the treatment regimen.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".