Re: “Investment Long Overdue in Primary Studies of HIV-exposed Uninfected Infant Infectious Morbidity”
Bibliographic record
Abstract
To the Editors: We thank Slogrove and Powis1 for their comments on our study,2 and we strongly agree that original research in this population is urgently needed. However, that does not discount the potential value of secondary analyses, so long as the limitations of this approach are clearly stated and accounted for. Much is still not understood about infectious morbidity in the HIV-exposed uninfected (HEU) population, nor for how long infectious morbidity risks continue.3,4 As Slogrove and Powis1 point out, HEU status has well-known effect on infectious illness within the first 6 months, and this alone may have implications later in life. Slogrove et al’s5 systematic review of infectious morbidity in HEU children identified 22 studies, of which 3 studies evaluated children 6 months and older. Furthermore, the consequences of recurrent infectious illness on growth (especially linear growth) is an area that has received little attention within HEU research, despite established evidence of its importance in the HIV-unexposed population. As stated in Slogrove et al’s5 review, studies “have not consistently considered confounding by universal infant risk factors of infectious morbidity and mortality; therefore, the role of direct HIV-exposure on infectious disease susceptibility is unclear.” We considered clinical markers of severity as universal risk factors for infectious morbidity and mortality, not specifically associated with either HIV exposure, pneumonia related to HIV exposure or on the causal pathway. Using that reasoning, we did adjust for clinical markers of severity in our study. We have now repeated the multivariable analysis, omitting Blantyre Coma Score and oxygen saturation; the results are not materially changed [odds ratio (OR) for death for HEU: 1.02; 95% confidence interval (CI): 0.41–2.50]. In addition, HEU children may have factors independent of illness severity at presentation that predispose them to poor outcome. It is also worth pointing out that on univariate analysis, none of the clinical parameters obtained on admission were significantly associated with HEU status. What we also controlled for, and would regard one of the strengths of our secondary analysis, were important social and environmental issues that could be confounders, such as number of household members and maternal education, and we continued to find a consistent effect across anthropometric parameters when we did so. On the final point by Slogrove and Powis1, we are in agreement; that further studies designed specifically for the HEU population need to be considered and conducted, if we are to advance the science. Investment in studies primarily designed and appropriately powered to elucidate the HEU population however depends on funding. We hope the study we have conducted has improved understanding but also increased awareness that many questions still remain unanswered. It remains imperative that we understand how the clinical presentation, risk factors and trajectory of HEU children are distinct from HIV-unexposed individuals so that we can target this growing population for specific interventions. Pui-Ying Iroh Tam, MDMalawi-Liverpool Wellcome Trust Clinical Research ProgrammeBlantyre, Malawi Matthew O. Wiens, PhDDepartment of Pediatrics, University of British ColumbiaVancouver, British Columbia, Canada Peter P. Moschovis, MDDivision of Global Health, Massachusetts General HospitalBoston, Massachusetts
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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 | 0.067 | 0.351 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.011 | 0.004 |
| Research integrity | 0.030 | 0.044 |
| Insufficient payload (model declined to judge) | 0.023 | 0.017 |
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".