Reply to Slogrove et al
Bibliographic record
Abstract
We would like to thank Slogrove and colleagues for the positive comments on our manuscript and for emphasizing the need to provide definitive evidence of the benefit of controlling maternal human immunodeficiency virus (HIV) infections for the health of infants born in low- and middle-income countries (LMIC), where the burden of HIV infection is highest [1]. We agree that the pathways leading to the vulnerability of HIV-exposed, uninfected (HEU) infants may not be identical in LMIC and in high-income countries (HIC), and that the potential role of specific factors has to be determined. As proposed by Slogrove and colleagues, the decreased risk of hospitalization observed in our study for those infections associated with the initiation of antiretroviral therapy (ART) before pregnancy may be offset by an increased risk of premature delivery in women living in LMIC [2]. On the other hand, contrary to HIC, women living with HIV in LMIC are encouraged to breastfeed. Although the evidence from HIC is less consistent [3], there is strong supportive evidence for a protective effect of breastfeeding on infectious morbidity in LMIC [4]. Through a diversity of immunological components, breastfeeding could reduce the immunological risk of severe infections after birth and thereby mitigate the impact of immune alterations induced by in utero exposure to maternal HIV infection. In our study, maternal and newborn immune activation predicted the risk of hospitalization due to infection in infants born to mothers who initiated ART during pregnancy [5]. Immune activation is commonly observed in adults living in LMIC, independently of HIV infection [6]. Therefore, the potential for ART to correct immune activation in women living with HIV may be lower in LMIC as compared to HIC, and this could mitigate the impact of ART initiation before pregnancy on infants’ susceptibility to infectious diseases. Although the vulnerability of HEU infants living in different settings could involve different factors, it is essential to recognize that this vulnerability is a global public health issue. An increased susceptibility of HEU infants to severe infections is observed in both LMIC and HIC, suggesting that common determinants are playing a critical role [7, 8]. Identifying these determinants has the potential to positively impact the health of HEU infants worldwide. To meet this challenge, researchers in HIC and LMIC should join efforts and integrate both intensive studies on relatively small study populations and larger studies that are powered to determine the impact of key environmental factors on clinical outcomes. Control of maternal HIV infection before pregnancy and progress in our understanding of the immunobiology of infant exposure to maternal HIV infection provide unprecedented opportunities to further improve the health of children born to HIV-infected mothers. Potential conflicts of interest. A. M.’s institution has received fees from GlaxoSmithKline Vaccines, outside the submitted work. G. A.’s institution has received grants from Gilead Sciences and the Bill and Melinda Gates Foundation (grant numbers OPP1032817, OPP1097381, and OPP1114729). T. R. K.’s institution has received grants from the National Institute for Allergy and Infectious Diseases and the Canada Institutes for Health Research. All other authors report no potential conflicts. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.
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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 | 0.003 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.052 | 0.032 |
| Insufficient payload (model declined to judge) | 0.011 | 0.012 |
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".