The value and impact of weight reduction from the perspective of people with type 2 diabetes in the <scp>United States</scp>
Bibliographic record
Abstract
AIMS: To understand the importance and potential impacts of reaching a lower weight or body mass index (BMI) from the perspectives of people with type 2 diabetes (T2D) across BMI levels. MATERIALS AND METHODS: A cross-sectional survey, informed by qualitative interviews, was administered to a sample representative of the US T2D population by BMI. The survey asked about experience and perceptions of weight management, the impact of T2D on quality of life (QoL), and the value of 5%, 10%, and 20% weight reductions. Results were summarised descriptively. RESULTS: [SD: 8.4]), 47% had a current haemoglobin A1c <7%. T2D affected QoL with impacts on comorbidities (39%), health complications (29%), emotional well-being (29%), and daily activities (28%). Most reported that the impact of weight on T2D was an issue (89%), they needed to lose weight (87%), and they struggled to lose weight (76%). Nearly all felt weight management was important to managing their T2D (93%), and 5%/10%/20% weight reductions would positively impact their perception of their future with T2D (60%/74%/69%, respectively). The majority felt 5%/10%/20% weight reductions would have positive impacts (72%/79%/70%, respectively), specifically on their appearance (70%/81%/83%, respectively), comorbidities (59%/72%/80%, respectively), and emotional well-being (51%/58%/66%, respectively). CONCLUSIONS: People with T2D, across BMI categories, place considerable value on the opportunity for weight reduction yet endure considerable challenges in doing so through lifestyle interventions, even when weight reduction is discussed with their healthcare provider.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".