The impact on utilities of differences in body weight among Canadian patients with type 2 diabetes
Bibliographic record
Abstract
The objective of this study was to estimate utility values for hypothetical health states that describe differences in weight and quality of life associated with type 2 diabetes mellitus (DM) from Canadians with type 2 DM. The impact on utility values was examined separately for participants with a body mass index (BMI) of 18 to less than 25 kg/m2 (‘healthy’), 25 to less than 30 (‘overweight’), and 30 or more (‘obese’). The health state descriptions were modified from a published diabetes utility study. Health states included a base-case type 2 DM health state (at participants’ current weight), and six health states where the weight and attendant quality of life impact varied (base case ±3%, ±5%, and ±7% weight). Utilities were elicited using the time trade-off technique. Linear regression modeling was used to estimate the utility increment or decrement associated with a one unit difference in BMI. Among 96 participants, the mean age was 55 years and 51% were men. The mean BMI was 32 kg/m2 and 84% wanted to lose weight. The mean (SD) utility for the base-case state was 0.911 (0.013). Mean utilities (utility decrements) were 0.907 (−0.004), 0.865 (−0.046) and 0.806 (−0.105) for the health states describing an increased weight of 3%, 5% and 7%, respectively; and 0.923 (+0.012), 0.940 (+0.029) and 0.949 (+0.038) for the health states describing a decreased weight of 3%, 5% and 7%, respectively. For every increase of 1 kg/m2 BMI there was an associated decrease in utility of 0.0472 (95% CI: 0.0375, 0.0569) and for every decrease of 1 kg/m2 BMI there was an associated increase in utility of 0.0171 (95% CI: 0.0103, 0.0238). The preferences of Canadian patients with type 2 DM for diabetes-related health states varied according to the weight, and quality of life impact, associated with that health state. Increased weight had a greater effect on utilities than decreased weight.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".