Factors predictive of oxygen consumption during the immediate postoperative period in open heart surgery.
Bibliographic record
Abstract
BACKGROUND: Postoperative oxygen consumption (VO2) is critical during the recovery period that follows open heart surgery and depends on patient characteristics and surgical factors. OBJECTIVE: To explore the surgical and patient-related factors that may influence VO2 during the early postoperative period. DESIGN: Prospective study. SETTING: Postoperative intensive care unit. PATIENTS: Study participants were 50 consecutive patients undergoing elective open heart surgery. There were 39 men and 11 women, with a mean age of 58+/-10 years. MEASUREMENTS AND MAIN RESULTS: VO2, oxygen extraction and arterial lactate were measured 1, 4, 12 and 24 h postoperatively. VO2 increased significantly during the first 12 h and stabilized thereafter. Oxygen extraction remained stable through the first 24 h. Covariance analysis on repeated measures showed that the extracorporeal circulatory period (P<0.01), age (P<0.01), body temperature (P<0.05) and use of noradrenalin (P<0.05) were predictive factors influencing postoperative VO2. Although arterial lactate increased significantly during the first 12 h period, no correlation with VO2 was found. However, covariance analysis showed that female sex, patient age (older than 65 years) and bypass period were positive correlating factors for the increase in arterial lactate. CONCLUSIONS: Patient VO2 need is decreased early after open heart surgery and returns to normal after 12 h. Surgical and patient-specific factors are responsible for these changes. Arterial lactate measurements were not found to be reliable indexes of VO2 need during this period.
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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 | 0.000 | 0.003 |
| 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".