Defying Death: Can New ECMO Technology Improve the Outcomes of Postcardiotomy Shock?
Bibliographic record
Abstract
Postcardiotomy shock continues to be a devastating, but fortunately rare, complication after cardiac surgery.The definition of postcardiotomy shock remains variable, but generally reflects patients who have inadequate cardiac performance after surgery despite inotropic and intraaortic balloon pump support.We have previously described an objective definition of "low cardiac output syndrome" (LOS) which incorporates all patients who receive any form of postoperative inotropic or mechanical support for greater than 30 minutes in the intensive care unit (ICU). 1 Patients with severe LOS, defined as inadequate end-organ perfusion despite maximal medical support, are often put forth for consideration of mechanical circulatory support.Samuels et al. have previously reported the risk of in-hospital death based upon the degree of inotropic support in intraaortic balloon pump (IABP)-supported patients.2 Patients who require three "high-dose" inotropes in addition to IABP support face an in-hospital mortality of greater than 70%.Thus, many clinicians agree to provide mechanical support to this group in an effort to reduce this high mortality rate.The report by Pokersnik et al. from the Cleveland Clinic in this issue of the Journal examines the impact of changing ECMO technology on clinical outcomes in this challenging patient population.3 This report reviews over 238 ECMO recipients in a six-year period of which 49 satisfied the inclusion criteria of the authors.These 49 patients were restricted to in-house insertion of ECMO for postcardiotomy shock and excluded those patients who underwent emergency surgery and/or solid organ transplantation.Sadly, the authors demonstrate that postcardiotomy shock is
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.024 | 0.020 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".