Sarcopenia and malnutrition: worthwhile prehabilitation targets?
Bibliographic record
Abstract
Despite advances in surgical techniques and perioperative care, complications can beget prolonged hospital stays and increased morbidity and mortality [1–3]. Sarcopenia, characterized by loss of muscle mass and strength, and malnutrition are recognized as key surgical risk factors, particularly in older patients [4]. While malnutrition can exacerbate sarcopenia, the exact relationship between these conditions remains poorly understood [3, 4]. Abe et al.’s study, published in this issue of the European Journal of Cardio-Thoracic Surgery, provides valuable insights into the impact of these preoperative conditions on early postoperative outcomes. The recent tri-society statement from the European Association for Cardio-Thoracic Surgery (EACTS) and the European Association of Preventive Cardiology (EAPC) of the European Society of Cardiology (ESC) has advocated and provided guidance on pre-interventional frailty assessment [5]. Key components of this preoperative assessment are evaluation of sarcopenia and malnutrition. Despite the awareness of the association and contribution of these preoperative vulnerabilities, these conditions are often undiagnosed in clinical practice, highlighting a disconnect between the evidence and its application in patient care [1, 6–8]. Several barriers contribute to this gap. First, the absence of universal diagnostic criteria for sarcopenia in cardiac surgery patients complicates its identification and limits cross-study comparability [1]. Second, there is no consensus on the best tools for assessing these conditions in surgical patients. Without clear guidelines, clinicians may be uncertain about incorporating these evaluations into preoperative workflows [4]. Third, time and resource constraints, including the need for specialized personnel and equipment, often hinder routine screening for sarcopenia and malnutrition. The current article by Abe et al. seeks to, in part, address this 3rd barrier.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.038 | 0.031 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".