Postoperative Weight Gain within Enhanced Recovery after Cardiac Surgery
Bibliographic record
Abstract
Optimal fluid therapy during perioperative care as part of enhanced recovery after cardiac surgery (ERACS) should improve the outcome. Our objective was finding out the effects of fluid overload on outcome and mortality within a well-established ERACS program. All consecutive patients undergoing cardiac surgery between January 2020 and December 2021 were enrolled. According to ROC curve analysis, a cut-off of ≥7 kg (group M, n = 1198) and <7 kg (group L, n = 1015) was defined. A moderate correlation was shown between weight gain and fluid balance r = 0.4, and a simple linear regression was significant p < 0.0001, R2 = 0.16. Propensity score matching showed that increased weight gain was associated with a longer hospital length of stay (LOS) (L 8 [3] d vs. M 9 [6] d, p < 0.0001), an increased number of patients who received pRBCs (L 311 (36%) vs. M 429 (50%), p < 0.0001), and a higher incidence of postoperative acute kidney injury (AKI) (L 84 (9.8%) vs. M 165 (19.2%), p < 0.0001). Weight gain can easily represent fluid overload. Fluid overload after cardiac surgery is common and is associated with prolonged hospital LOS and increases the incidence of AKI.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".