Variations in <scp>CD4</scp> counts during pregnancy in women living with <scp>HIV</scp>
Bibliographic record
Abstract
Abstract Objective Our objective was to determine the frequency at which CD4 counts drop below 200 cells/mm 3 during pregnancy in women living with HIV and to identify factors associated with this. Methods Data from 2005 to 2020 from two prospective Canadian cohorts of pregnant women living with HIV were extracted. As per national guidelines, women received antiretroviral therapy and CD4 counts were monitored once per trimester and at delivery. Results Among 775 included cases, 72 (9.3%) had CD4 counts <200 cells/mm 3 at the first pregnancy visit. Of the 703 remaining pregnancies with CD4 counts ≥200 cells/mm 3 at the initial visit, 20 (2.8%) were associated with a drop to <200 cells/mm 3 . In univariate analysis, factors associated with this drop were coinfection with hepatitis B virus or hepatitis C virus (odds ratio [OR] 4.0, 95% confidence interval [CI] 1.52–10.50), lower first visit CD4 counts (OR 0.165, 95% CI 0.08–0.34), and baseline haemoglobin levels <11 g/dL (OR 2.89, 95% CI 1.04–8.00). In multivariable analysis, only CD4 count at first visit remained independently associated with this drop. A cut‐off CD4 count ≤450 cells/mm 3 at the first pregnancy visit had a sensitivity of 100% to detect cases of CD4 drop to <200 cells/mm 3 . Conclusion A drop of CD4 count to <200 cells/mm 3 is uncommon during pregnancy in women living with HIV. Our results suggest that CD4 monitoring only once in pregnancy would be safe in women whose CD4 count is >450 cells/mm 3 at the first pregnancy visit.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".