Effect of pregnancy on immunological and virological outcomes of women on ART: a prospective cohort study in rural Uganda, 2004–2009
Bibliographic record
Abstract
OBJECTIVES: Before antiretroviral therapy (ART) introduction, pregnancy was associated with a sustained drop in CD4 cell count in HIV-infected women. We examined the effects of pregnancy on immunological and virological ART outcomes. METHODS: Between January 2004 and March 2009, we studied HIV-infected women receiving ART in a prospective open cohort study in rural Uganda. We used random effects regression models to compare the CD4 counts of women who became pregnant and those who did not, and among the pregnant women before and after pregnancy. CD4 count and proportions with detectable viral load (≥400 copies/ml) were compared between the two groups using the Mann-Whitney rank sum test and logistic regression respectively. RESULTS: Of 88 women aged 20-40 years receiving ART, 23 became pregnant. At ART initiation, there were no significant differences between those who became pregnant and those who did not in clinical, immunological and virological parameters. Among women who became pregnant, CD4 cell count increased before pregnancy (average 75.9 cells/mm(3) per year), declined during pregnancy (average 106.0) but rose again in the first year after delivery (average 88.6). Among women who did not become pregnant, the average CD4 cell count rise per year for the first 3 years was 88.5. There was no significant difference in the proportions of women with detectable viral load at last clinic visit among those who became pregnant (8.7%) and those who did not (16.1%), P = 0.499. CONCLUSION: Pregnancy had no lasting effect on the immunological and virological outcomes of HIV-infected women on ART.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".