Scoring Systems for Organ Dysfunction and Multiple Organ Dysfunction: The PODIUM Consensus Conference
Bibliographic record
Abstract
CONTEXT: Multiple scores exist to characterize organ dysfunction in children. OBJECTIVE: To review the literature on multiple organ dysfunction (MOD) scoring systems to estimate severity of illness and to characterize the performance characteristics of currently used scoring tools and clinical assessments for organ dysfunction in critically ill children. DATA SOURCES: Electronic searches of PubMed and Embase were conducted from January 1992 to January 2020. STUDY SELECTION: Studies were included if they evaluated critically ill children with MOD, evaluated the performance characteristics of scoring tools for MOD, and assessed outcomes related to mortality, functional status, organ-specific outcomes, or other patient-centered outcomes. DATA EXTRACTION: Data were abstracted into a standard data extraction form by a task force member. RESULTS: Of 1152 unique abstracts screened, 156 full text studies were assessed including a total of 54 eligible studies. The most commonly reported scores were the Pediatric Logistic Organ Dysfunction Score (PELOD), pediatric Sequential Organ Failure Assessment score (pSOFA), Pediatric Index of Mortality (PIM), PRISM, and counts of organ dysfunction using the International Pediatric Sepsis Definition Consensus Conference. Cut-offs for specific organ dysfunction criteria, diagnostic elements included, and use of counts versus weighting varied substantially. LIMITATIONS: While scores demonstrated an increase in mortality associated with the severity and number of organ dysfunctions, the performance ranged widely. CONCLUSIONS: The multitude of scores on organ dysfunction to assess severity of illness indicates a need for unified and data-driven organ dysfunction criteria, derived and validated in large, heterogenous international databases of 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 | 0.053 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".