Understanding the Global Epidemiology of Pediatric Critical Illness
Bibliographic record
Abstract
OBJECTIVE: The point prevalence methodology is a valuable epidemiological study design that can optimize patient enrollment, prospectively gather individual-level data, and measure practice variability across a large number of geographic regions and healthcare settings. The objective of this article is to review the design, implementation, and analysis of recent point prevalence studies investigating the global epidemiology of pediatric critical illness. DATA SOURCES: Literature review and primary datasets. STUDY SELECTION: Multicenter, international point prevalence studies performed in PICUs since 2007. DATA EXTRACTION: Study topic, number of sites, number of study days, patients screened, prevalence of disease, use of specified therapies, and outcomes. DATA SYNTHESIS: Since 2007, five-point prevalence studies have been performed on acute lung injury, neurologic disease, thromboprophylaxis, fluid resuscitation, and sepsis in PICUs. These studies were performed in 59-120 sites in 7-28 countries. All studies accounted for seasonal variation in pediatric disease by collecting data over multiple study days. Studies screened up to 6,317 patients and reported data on prevalence and therapeutic variability. Three studies also reported short-term outcomes, a valuable but atypical data element in point prevalence studies. Using these five studies as examples, the advantages and disadvantages and approach to designing, implementing, and analyzing point prevalence studies are reviewed. CONCLUSIONS: Point prevalence studies in pediatric critical care can efficiently provide valuable insight on the global epidemiology of disease and practice patterns for critically ill children.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".