Replication Data for: Systemic biological mechanisms underpin poor post-discharge growth among severely wasted children with HIV
Bibliographic record
Abstract
This is a replication dataset for the manuscript titled: "Systemic biological mechanisms underpin poor post-discharge growth among severely wasted children with HIV." In sub-Saharan Africa, a proportion of children hospitalised with severe malnutrition (SM) also have HIV infection (HIV-SM). Children with HIV-SM have poorer clinical outcomes than children with SM alone. They face high mortality both during and after hospitalisation, have impaired nutritional recovery post-hospitalisation and have increased relapse recovery. Despite this elevated risks biological mechanisms underlying the risk remain unclear. This study is nested with the CHAIN cohort sites in Kenya, Uganda, Malawi and Burkina Faso. The current study aimed to understand how HIV influences post-discharge growth among children with HIV-SM in sub-Saharan Africa. In the current study proteins from plasma collected from children at hospital discharge were quantified using SomaScan assay. Over 7300 proteins were quantified. The analysis also included anthropometric measurements e.g., mid-upper arm circumference, weight-for-age, weight-for-height and height-for-age scores taken at discharge, 45-days post-discharge, 90-days post-discharge, and 180-days post-discharge. Demographic data included sex, age, site of enrolment.
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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 | 0.010 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.177 | 0.039 |
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".