Correlation of postoperative fluid balance and weight and their impact on outcomes
Bibliographic record
Abstract
INTRODUCTION: Normovolemia after major surgery is critical to avoid complications. The aim of the present study was to analyze correlation between fluid balance, weight gain, and postoperative outcomes. METHODS: All consecutive patients undergoing elective or emergency major abdominal surgery needing intermediate care unit (IMC) admission from September 2017 to January 2018 were included. Postoperative fluid balances and daily weight changes were calculated for postoperative days (PODs) 0-3. Risk factors for postoperative complications (30-day Clavien) and prolonged length of IMC and hospital stay were identified through uni- and multinominal logistic regression. RESULTS: One hundred eleven patients were included, of which 55% stayed in IMC beyond POD 1. Overall, 67% experienced any complication, while 30% presented a major complication (Clavien ≥ III). For the entire cohort, median cumulative fluid balance at the end of PODs 0-1-2-3 was 1850 (IQR 1020-2540) mL, 2890 (IQR 1610-4000) mL, 3890 (IQR 2570-5380) mL, and 4000 (IQR 1890-5760) mL respectively, and median weight gain was 2.2 (IQR 0.3-4.3) kg, 3 (1.5-4.7) kg, and 3.9 (2.5-5.4) kg, respectively. Fluid balance and weight course showed no significant correlation (r = 0.214, p = 0.19). Extent of surgery, analyzed through Δ albumin and duration of surgery, significantly correlated with POD 2 fluid balances (p = 0.04, p = 0.006, respectively), as did POD 3 weight gain (p = 0.042). Prolonged IMC stay of ≥ 3 days was related to weight gain ≥ 3 kg at POD 2 (OR 2.8, 95% CI 1.01-8.9, p = 0.049). CONCLUSION: Fluid balance and weight course showed only modest correlation. POD 2 weight may represent an easy and pragmatic tool to optimize fluid management and help to prevent fluid-related postoperative complications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".