Cardiogenic shock: a major challenge for the clinical trialist
Bibliographic record
Abstract
PURPOSE OF REVIEW: Cardiogenic shock (CS) results in persistently high short-term mortality and a lack of evidence-based therapies. Several trials of novel interventions have failed to show an improvement in clinical outcomes despite promising preclinical and physiologic principles. In this review, we highlight the challenges of CS trials and provide suggestions for the optimization and harmonization of their design. RECENT FINDINGS: CS clinical trials have been plagued by slow or incomplete enrolment, heterogeneous or nonrepresentative patient cohorts, and neutral results. To achieve meaningful, practice-changing results in CS clinical trials, an accurate CS definition, a pragmatic staging of its severity for appropriate patient selection, an improvement in informed consent process, and the use of patient-centered outcomes are required. Future optimizations include the use of predictive enrichment using host response biomarkers to unravel the biological heterogeneity of the CS syndrome and identify subphenotypes most likely to benefit from individualized treatment to allow a personalized medicine approach. SUMMARY: Accurate characterization of CS severity and its pathophysiology are crucial to unravel heterogeneity and identify the patients most likely to benefit from a tested treatment. Implementation of biomarker-stratified adaptive clinical trial designs (i.e., biomarker or subphenotype-based therapy) might provide important insights into treatment effects.
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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 | 0.033 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".