Gestational weight gain in persons with HIV in the United States
Bibliographic record
Abstract
OBJECTIVE: We evaluated gestational weight gain (GWG) in pregnant persons with HIV (PWH) enrolled in the Surveillance Monitoring for ART Toxicities study. DESIGN: This was a cohort study. METHODS: GWG was classified as excessive, adequate, or inadequate; weekly GWG in second and third trimesters was calculated using National Academy of Medicine standards. Adjusted modified Poisson and linear regression models were fit with generalized estimating equations to assess the association of antiretroviral treatment (ART) with GWG outcomes stratified by timing of ART initiation [at conception (ART-C) and initiating during pregnancy (ART-I)]. RESULTS: We included 1477 pregnancies (847 ART-C, 630 ART-I) from 1282 PWH. The proportion of excessive, adequate, and inadequate GWG was 44, 24, and 32%, respectively. No associations of ART class with excessive GWG were observed overall. However, among ART-I pregnancies with overweight prepregnancy BMI-based, protease inhibitor-based, nonnucleoside reverse transcriptase inhibitor-based, and nucleoside reverse transcriptase inhibitor-based ART were associated with significantly lower GWG per week than integrase inhibitor (INSTI)-based ART [mean differences: -0.14, -0.27, and -0.29 kg/week, respectively]. Among ART-I pregnancies with obese prepregnancy BMI, lower weekly GWG was also observed for protease inhibitor-based vs. INSTI-based ART (mean difference: -0.14 kg/week). CONCLUSION: ART class type was not associated with excessive GWG. However, PWH entering pregnancy already overweight/obese and initiating INSTI-based ART had higher weekly GWG in second and third trimesters vs. other ART classes. Further studies to understand how increases in weekly GWG for overweight/obese PWH impinges on long-term maternal/child health are warranted.
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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 | 0.002 |
| 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 | 0.000 | 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".