High Mobility Group Box-1 Protein and Outcomes in Critically Ill Surgical Patients Requiring Open Abdominal Management
Bibliographic record
Abstract
Background . Previous studies assessing various cytokines in the critically ill/injured have been uninformative in terms of translating to clinical care management. Animal abdominal sepsis work suggests that enhanced intraperitoneal (IP) clearance of Damage-Associated Molecular Patterns (DAMPs) improves outcome. Thus measuring the responses of DAMPs offers alternate potential insights and a representative DAMP, High Mobility Group Box-1 protein (HMGB-1), was considered. While IP biomediators are being recognized in critical illness/trauma, HMGB-1 behaviour has not been examined in open abdomen (OA) management. Methods . A modified protocol for HMGB-1 detection was used to examine plasma/IP fluid samples from 44 critically ill/injured OA patients enrolled in a randomized controlled trial comparing two negative pressure peritoneal therapies (NPPT): Active NPPT (ANPPT) and Barker’s Vacuum Pack NPPT (BVP). Samples were collected and analyzed at the time of laparotomy and at 24 and 48 hours after. Results . There were no statistically significant differences in survivor versus nonsurvivor HMGB-1 plasma or IP concentrations at baseline, 24 hours, or 48 hours. However, plasma HMGB-1 levels tended to increase continuously in the BVP cohort. Conclusions . HMGB-1 appeared to behave differently between NPPT cohorts. Further studies are needed to elucidate the relationship of HMGB-1 and outcomes in septic/injured patients.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".