Postoperative Pain Significantly Influences Postoperative Blood Loss in Patients Undergoing Total Knee Replacement
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Although hypertension has long been recognized as a factor that might increase intraoperative blood losses in major orthopedic surgery, the effects of postoperative pain-induced hypertension on blood losses have not so far been evaluated. The aim of this study was to evaluate the effect of pain on perioperative blood losses of patients undergoing primary total knee replacement (TKR). METHODS: Data from patients participating in a randomized clinical trial comparing intravenous patient-controlled analgesia (PCA) (N = 20) with PCA plus continuous femoral nerve (three-in-one) block (N = 20) or PCA plus continuous posterior lumbar plexus (psoas compartment) block (N = 20) were prospectively and retrospectively collected. Correlations between relevant variables and measured and calculated blood loss, number of transfused unit, and late (96 hours) postoperative hemoglobin were tested by linear regressions. Stepwise regressions for each of the four above-mentioned goals were constructed using a probability to enter of 0.25 and to leave of 0.1. A P < 0.05 was considered significant. RESULTS: At the stepwise regressions there was a significant positive correlation between measured blood losses and morphine consumption from 12 to 18 hours (P = 0.006); between calculated blood loss and preoperative mean arterial blood pressure (P = 0.01) and preoperative hemoglobin value (P = 0.02); and between late postoperative hemoglobin and body weight (P = 0.047). CONCLUSION: In patients undergoing TKR, there is a significant correlation between measured blood loss and morphine consumption from 12 to 18 hours. It is concluded that postoperative pain significantly influences postoperative blood loss in patients undergoing TKR.
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.
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 | 0.004 |
| 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".