Lack of Health-Related Quality of Life and Patient-Centered Outcome Measures in RCTs Conducted for Diabetes Pharmacotherapy: SGLT-2 receptor inhibitors as an example
Bibliographic record
Abstract
BACKGROUND: Use of SGLT-2 receptor inhibitors has been associated with weight loss and a low rate of hypoglycemia in comparison to sulfonylureas. These factors may improve health-related quality of life for patients with diabetes. OBJECTIVES: To systematically explore randomized controlled trials (RCTs) involving SGLT-2 receptor inhibitors that reported health-related quality of life changes. METHODS: A systematic review of SGLT-2 receptor inhibitors clinical trials, limited to RCTs and English language, was conducted utilizing PubMed databases. RESULTS: One-hundred and eighteen RCTs were reviewed and 62 RCTs meeting the inclusion criteria were assessed. All 62 RCTs reported body mass index (BMI) changes and HgbA1c reduction. Measures of health-related quality-of-life (HRQoL) were reported in only 2 RCTs. Both studies illustrate improvement in HRQoL domains for SGLT-2 receptor inhibitors in comparison to the other arms in the RCTs. CONCLUSIONS: Only a small portion of RCTs involving SGLT-2 receptor inhibitors reported on HRQoL. Because of the potential for weight loss and hypoglycemia avoidance to improve HRQoL, future studies of SGLT-2 receptor inhibitors should measure and report on patient-centred outcomes such as HRQoL.
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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 | 0.123 | 0.310 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".