Managing the first 120 min of cardiogenic shock: from resuscitation to diagnosis
Bibliographic record
Abstract
PURPOSE OF REVIEW: Cardiogenic shock continues to carry a high mortality, and recent randomized trials have not identified novel therapies that improve survival. Early optimization of patients with confirmed or suspected cardiogenic shock is crucial, as patients can quickly transition from a hemodynamic shock state to a treatment-resistant hemometabolic shock state, where accumulated metabolic derangements trigger a self-perpetuating cycle of worsening shock. RECENT FINDINGS: We describe a structured ABCDE approach involving stabilization of the airway, breathing and circulation, followed by damage control and etiologic assessment. Respiratory failure is common and many cardiogenic shock patients require invasive mechanical ventilation. Norepinephrine is titrated to restore mean arterial pressure and dobutamine is titrated to restore cardiac output and organ perfusion. Echocardiography is essential to identify potential causes and characterize the phenotype of cardiogenic shock. Coronary angiography is usually indicated, particularly when acute myocardial ischemia is suspected, followed by culprit-vessel revascularization if indicated. An invasive hemodynamic assessment can clarify whether temporary mechanical circulatory support is necessary. SUMMARY: Early stabilization of hemodynamics and end-organ function is necessary to achieve best outcomes in cardiogenic shock. Using a structured approach tailored to initial cardiogenic shock resuscitation may help to demonstrate benefit from novel therapies in the future.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".