Expert Consensus of Data Elements for Collection for Enhanced Recovery After Cardiac Surgery
Bibliographic record
Abstract
BACKGROUND: Despite the emergence of Enhanced Recovery Protocols (ERPs) in cardiac surgery, there is no consensus on the essential elements for data reporting for quality improvement efforts, as well as accountability and standardization of outcome reporting across institutions. The aim of this study was to establish a consensus on essential data elements for cardiac ERAS®. METHODS: A 2-round modified Delphi technique was utilized based on existing recommendations from the recently published ERAS® cardiac surgery consensus guidelines. Round 1 included a steering committee of 10 experts who oversaw formulation of a focused list of data elements into 3 main areas: Preoperative, intraoperative and postoperative. Round 2 consisted of a multidisciplinary, multinational, heterogenous group of 50 voting experts from across the United States and Europe. All participants evaluated their level of agreement with each data element using a 5-point Likert scale with consensus threshold of 70%. RESULTS: In round 1, 17 data elements were considered essential (consensus > = 70%, either positive or negative) and 6 were considered marginal (consensus < = 70%, either positive or negative). In round 2, positive consensus was achieved for 15/17 (88.2%) data elements in the essential category, and all six data elements (100%) in the marginal category, indicating a high level of overall agreement. CONCLUSION: This initial study, which identified 21 key data elements for collection in an ERAS® cardiac program, will aid clinicians in establishing a framework for evaluating the quality of their contemporary ERP processes and will allow acquisition of data to help benchmark performance metrics between hospitals.
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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 | 0.364 | 0.351 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".