Weakness acquired in the cardiac intensive care unit: still the elephant in the room?
Bibliographic record
Abstract
Over the past two decades, the cardiac critical care population has shifted to increasingly comorbid and elderly patients often presenting with nonprimary cardiac conditions that exacerbate underlying advanced cardiac disease. Consequently, the modern cardiac intensive care unit (CICU) patient has poor outcome regardless of left ventricular ejection fraction. Importantly, delayed liberation from organ support, independent from premorbid health status and admission severity of illness, has been associated with increased morbidity and mortality up to years post-general critical care. Although a constellation of several acquired morbidities is at play, the most prominent enactor of poor long-term outcome in this population appears to be intensive care unit acquired weakness. Although the specific burden of ICU-acquired morbidities in CICU patients is yet to be clearly defined, it seems unfathomable that patients will not accrue some sort of ICU-related morbidity. There is hence an urgent need to better establish the exact benefit and cost of resource-intensive strategies in both short- and long-term survival of the CICU patient. Consequent and standardized documentation of admission comorbidities, severity of illness indicators, relevant ICU-related complications including weakness, and long-term post-ICU morbidity outcomes can help our understanding of the disease continuum and how to better care for the CICU survivor and their families and caregivers. Given increasing budgetary pressure on healthcare systems worldwide, interventions targeting CICU patients should focus on improving patient-centred long-term outcomes in a cost-effective manner. It will require a holistic and transmural continuity of care model to meet the challenges associated with treating critically ill cardiac patients 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".