Monitoring Severity of Multiple Organ Dysfunction Syndrome: New Technologies
Bibliographic record
Abstract
OBJECTIVE: To describe new technologies (biomarkers and tests) used to assess and monitor the severity and progression of multiple organ dysfunction syndrome in children as discussed as part of the Eunice Kennedy Shriver National Institute of Child Health and Human Development MODS Workshop (March 26-27, 2015). DATA SOURCES: Literature review, research data, and expert opinion. STUDY SELECTION: Not applicable. DATA EXTRACTION: Moderated by an experienced expert from the field, investigators developing and assessing new technologies to improve the care and understanding of critical illness presented their research and the relevant literature. DATA SYNTHESIS: Summary of presentations and discussion supported and supplemented by relevant literature. CONCLUSIONS: There are many innovative tools and techniques with the potential application for the assessment and monitoring of severity of multiple organ dysfunction syndrome. If the reliability and added value of these candidate technologies can be established, they hold promise to enhance the understanding, monitoring, and perhaps, treatment of multiple organ dysfunction syndrome in children.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".