Understanding Disparities in the Pediatric ICU: A Scoping Review
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Health disparities are pervasive in pediatrics. We aimed to describe disparities among patients who are likely to be cared for in the PICU and delineate how sociodemographic data are collected and categorized. METHODS: Using MEDLINE as a data source, we identified studies which included an objective to assess sociodemographic disparities among PICU patients in the United States. We created a review rubric, which included methods of sociodemographic data collection and analysis, outcome and exposure variables assessed, and study findings. Two authors reviewed every study. We used the National Institute on Minority Health and Health Disparities Research Framework to organize outcome and exposure variables. RESULTS: The 136 studies included used variable methods of sociodemographic data collection and analysis. A total of 30 of 124 studies (24%) assessing racial disparities used self- or parent-identified race. More than half of the studies (52%) dichotomized race as white and "nonwhite" or "other" in some analyses. Socioeconomic status (SES) indicators also varied; only insurance status was used in a majority of studies (72%) evaluating SES. Consistent, although not uniform, disadvantages existed for racial minority populations and patients with indicators of lower SES. The authors of only 1 study evaluated an intervention intended to mitigate health disparities. Requiring a stated objective to evaluate disparities aimed to increase the methodologic rigor of included studies but excluded some available literature. CONCLUSIONS: Variable, flawed methodologies diminish our understanding of disparities in the PICU. Meaningfully understanding and addressing health inequity requires refining how we collect, analyze, and interpret relevant data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.027 | 0.024 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".