Effect of early addition of rosiglitazone to sulphonylurea therapy in older type 2 diabetes patients (>60 years): the Rosiglitazone Early vs. SULphonylurea Titration (RESULT) study
Bibliographic record
Abstract
AIM: To compare the efficacy, safety and tolerability of adding rosiglitazone (RSG) vs. sulphonylurea (SU) dose escalation in older type 2 diabetes mellitus (T2DM) patients inadequately controlled on SU therapy. METHODS: A total of 227 T2DM patients from 48 centres in the USA and Canada, aged > or =60 years, were randomized to receive RSG (4 mg) or placebo once daily in combination with glipizide 10 mg twice daily for 2 years in a double-blind, parallel-group study. Previous SU monotherapy was (1/4) to (1/2) maximum recommended dose for > or =2 months prior to screening with fasting plasma glucose (FPG) > or =7.0 and < or =13.9 mmol/l. Treatment options were individualized, and escalation of study medication was specifically defined. RESULTS: Disease progression (time to reach confirmed FPG > or =10 mmol/l while on maximum doses of both glipizide and study medication or placebo) was reported in 28.7% of patients uptitrating SU plus placebo compared with only 2.0% taking RSG and SU combination (p < 0.0001). RSG + SU significantly decreased HbA(1c), FPG, insulin resistance, plasma free fatty acids and medical care utilization and improved treatment satisfaction compared with uptitrated SU. CONCLUSIONS: Addition of RSG to SU in older T2DM patients significantly improved glycaemic control and reduced disease progression compared with uptitrated SU alone but without increasing hypoglycaemia. These benefits were associated with increased patient treatment satisfaction and reduced medical care utilization with regards to emergency room visits and length of hospitalization. Early addition of RSG is an effective treatment option for older T2DM patients inadequately controlled on submaximal SU monotherapy.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".