Elevated Lactate is Independently Associated with Adverse Outcomes Following Hepatectomy
Bibliographic record
Abstract
BACKGROUND: Arterial lactate is frequently monitored to indicate tissue hypoxia and direct therapy. We sought to determine whether early post-hepatectomy lactate (PHL) is associated with adverse outcomes and define factors associated with PHL. METHODS: Hepatectomy patients at a single institution from 2003 to 2012 with PHL available were included. Univariable and multivariable analyses examined factors associated with PHL and the relationship between PHL and 30-day major morbidity (Clavien grade III-V), 90-day mortality, and length of stay (LOS). RESULTS: Of 749 hepatectomies, 490 were included of whom 71.4% had elevated PHL (≥2 mmol/L). Cirrhosis (coefficient 0.31, p = 0.039), Charlson comorbidity index (coefficient 0.05, p < 0.001), major resections (coefficient 0.34, p < 0.001), procedure time (coefficient 0.08, p < 0.001), and blood loss (coefficient 0.11, p < 0.001) were associated with PHL. As lactate increased from <2 to ≥6 mmol/L, morbidity rose from 11.6 to 40.6%, and mortality from 0.7 to 22.7%. PHL was independently associated with 90-day mortality (OR 1.52 p < 0.001) and 30-day morbidity (OR 1.19, p = 0.002), but not LOS (rate ratio 1.03, p = 0.071). CONCLUSION: Patients with elevated PHL in the initial postoperative period should be carefully monitored due to increased risk of major morbidity and mortality. Further research on the impact of lactate-directed fluid therapy is warranted.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".