Association between perioperative fluid management and patient outcomes: a multicentre retrospective study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Postoperative complications increase hospital length of stay and patient mortality. Optimal perioperative fluid management should decrease patient complications. This study examined associations between fluid volume and noncardiac surgery patient outcomes within a large multicentre US surgical cohort. METHODS: Adults undergoing noncardiac procedures from January 1, 2012 to December 31, 2017, with a postoperative length of stay ≥24 h, were extracted from a large US electronic health record database. Patients were segmented into quintiles based on recorded perioperative fluid volumes with Quintile 3 (Q3) serving as the reference. The primary outcome was defined as a composite of any complications during the surgical admission and a postoperative length of stay ≥7 days. Secondary outcomes included in-hospital mortality, respiratory complications, and acute kidney injury. RESULTS: A total of 35 736 patients met the study criteria. There was a U-shaped pattern with highest (Q5) and lowest (Q1) quintiles of fluid volumes having increased odds of complications and a postoperative length of stay ≥7 days (Q5: odds ratio [OR] 1.51 [95% confidence interval {CI}: 1.30-1.74], P<0.001; Q1: OR 1.20 [95% CI: 1.04-1.38], P=0.011) compared with Q3. Patients in Q5 had greater odds of more severe acute kidney injury compared with Q3 (OR 1.52 [95% CI: 1.22-1.90]; P<0.001) and respiratory complications (OR 1.44 [95% CI: 1.17-1.77]; P<0.001). CONCLUSIONS: Both very high and very low perioperative fluid volumes were associated with an increase in complications after noncardiac surgery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it