The association of bone mineral density with HIV infection and antiretroviral treatment in women
Bibliographic record
Abstract
BACKGROUND: Low bone mineral density (BMD) has been reported in HIV-infected women and men. METHODS: We analysed cross-sectional BMD measured by regional dual X-ray absorptiometry at the lumbar spine (LS) and femoral neck (FN) in 152 HIV-negative and 274 HIV-positive (HIV+) women, adjusted for traditional low BMD risk factors. RESULTS: BMD was significantly lower in protease inhibitor (PI) users than in all other groups, and highest in HIV-negative women. In multivariate analyses the prevalence of T-score < -1.0 was significantly higher in the HIV+ women naive to antiretroviral therapy (ART; odds ratio [OR] 4.36, 95% confidence interval [CI] 1.61, 11.8) and the women receiving PI-containing HAART (OR 3.72, CI 1.43, 9.68), with a non-significant difference in non-PI HAART users (OR 2.43, CI 0.92, 6.45), compared with HIV-negative women. In pair-wise adjusted comparisons, BMD was lower in ART-naive than in HIV-negative women (1.22 versus 1.30 g/cm2 at LS; P = 0.004), in PI compared with non-PI HAART users (1.00 versus 1.05 g/cm2 at FN; P = 0.014) and with those ART-naive (1.00 versus 1.03 g/cm2 at FN; P = 0.146). Potential confounders, including duration of ART, prior treatment regimens and traditional risk factors for low BMD did not explain these differences. Longer lopinavir use was significantly correlated with lower BMD (r2 = -0.39, P = 0.024 and r2 = -0.46, P = 0.006 at LS and FN, respectively) and longer efavirenz use with higher BMD (r2 = +0.32, P = 0.004 at FN). CONCLUSIONS: HIV infection was associated with lower BMD in women, independent of the traditional risk factors for low BMD. PI-containing HAART compared with non-PI-containing HAART, and longer lopinavir use, were both associated with lower BMD, and efavirenz use was associated with higher BMD.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".