Surviving and Thriving—Shifting the Public Health Response to HIV-Exposed Uninfected Children: Report of the 3rd HIV-Exposed Uninfected Child Workshop
Bibliographic record
Abstract
Great gains were achieved with the introduction of the United Nations' Millennium Development Goals, including improved child survival. Transition to the Sustainable Development Goals (SDGs) focused on surviving, thriving, and transforming, representing an important shift to a broader public health goal, the achievement of which holds the promise of longer-term individual and societal benefits. A similar shift is needed with respect to outcomes for infants born to women living with HIV (WLHIV). Programming to prevent vertical HIV transmission has been successful in increasingly achieving a goal of HIV-free survival for infants born to WLHIV. Unfortunately, HIV-exposed uninfected (HEU) children are not achieving comparable health and developmental outcomes compared with children born to HIV-uninfected women under similar socioeconomic circumstances. The 3rd HEU Child Workshop, held as a satellite session of the International AIDS Society's 9th IAS Conference in Paris in July 2017, provided a venue to discuss HEU child health and development disparities. A summary of the Workshop proceedings follows, providing current scientific findings, emphasizing the gap in systems for long-term monitoring, and highlighting the public health need to establish a strategic plan to better quantify the short and longer-term health and developmental outcomes of HEU children.
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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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".