The pediatric multiple organ dysfunction syndrome
Bibliographic record
Abstract
OBJECTIVES: To review the epidemiology of pediatric multiple organ dysfunction syndrome (MODS) and summarize current concepts regarding the pathophysiology of shock, organ dysfunction, and nosocomial infections in this population. DATA SOURCE: A MEDLINE-based literature search using the keywords MODS and child, without any restriction to the idiom. MAIN RESULTS: Critically ill children may frequently develop multisystemic manifestations during the course of severe infections, multiple trauma, surgery for congenital heart defects, or transplantations. Descriptive scores to estimate the severity of pediatric MODS have been validated. Young age and chronic health conditions have also been recognized as important contributors to the development of MODS. Unbalanced inflammatory processes and activation of coagulation may lead to the development of capillary leak and acute respiratory distress syndrome. Neuroendocrine and metabolic responses may result in insufficient adaptive immune response and the development of nosocomial infections, which may further threaten host homeostasis. CONCLUSIONS: Over the last 20 yrs, there has been an increasing knowledge on the epidemiology of pediatric MODS and on the physiologic mechanisms involved in the genesis of organ dysfunction. Nevertheless, further studies are needed to more clearly evaluate what is the long-term outcome of pediatric MODS.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".