The Abdomen in “Thoracoabdominal” Cannot Be Ignored: Abdominal Compartment Syndrome Complicating Extracorporeal Life Support
Bibliographic record
Abstract
Extracorporeal life support (ECLS) is an incredible life-saving measure that is being used ever more frequently in the care of the critically ill. Management of these patients requires extreme vigilance on the part of the care providers in recognizing and addressing the complications and challenges that may arise. We present a case of overt abdominal compartment syndrome (ACS) in a previously well young male on ECLS with a history of trauma, submersion, hypothermia, and no intra-abdominal injuries. The patient developed ACS soon after ECLS was initiated which resulted in drastically compromised flow rates. Taking into account the patient's critical status, an emergent laparotomy was performed in the intensive care unit which successfully resolved the ACS and restored ECLS flow. The patient had an unremarkable course following and was weaned off ECLS but unfortunately died from his original anoxic injury. This case highlights several salient points: first, care of patients on ECLS is challenging and multiple etiologies can affect our ability to manage these patients; second, intra-abdominal pressures should be monitored liberally in the critically ill, especially in patients on ECLS; third, protocols for emergent operative treatment outside of traditional operating rooms should be established and care providers should be prepared for these situations.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".