A Meta-Analysis of Six Placebo-Controlled Trials of Thiazolidinedione Therapy for HIV Lipoatrophy
Bibliographic record
Abstract
OBJECTIVE: To determine the impact of thiazolidinediones (TZD) on changes in limb fat mass in HIV-infected individuals, particularly in those not receiving a thymidine analogue. METHODS: Individual patient data from placebo-controlled, randomized trials of rosiglitazone (n = 5) or pioglitazone (n = 1) were combined. Generalized estimating equation (GEE) models were used to estimate the treatment effect on changes in limb fat mass. RESULTS: In the combined dataset of 427 patients, the baseline median age was 45 years, 86% were male, 80% were Caucasian, 63% were receiving stavudine (d4T) or zidovudine (AZT), 66% were on protease inhibitors, and median body mass index was 23 kg/m(2). In a univariate GEE model, TZD was associated with an increase in limb fat mass (coeff = 0.14 kg vs placebo, P = .04). In a multivariable GEE model, patients receiving pioglitazone had significantly higher limb fat mass gains (coeff = 0.35 kg, P < or = .01) compared to patients receiving placebo, while patients on rosiglitazone did not (coeff = 0.05 kg, P = .48). Interactions between thymidine analogue use and rosiglitazone and pioglitazone were not significant. CONCLUSIONS: In this meta-analysis, pioglitazone therapy was more effective than placebo to increase limb fat mass whereas rosiglitazone was not significantly better than placebo. The effectiveness of these drugs did not vary according to whether the patients were receiving thymidine analogues.
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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 | 0.017 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.042 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".