Epidural Analgesia in the Post-Anaesthesia Care Unit
Bibliographic record
Abstract
Epidural analgesia is widely used for postoperative pain in a variety of surgical operations and it is recognised to provide superior quality of analgesia when compared with systemic opioids. The combination of low doses of local anaesthetics and opioids appears to provide optimal analgesia with minimal motor blockade. However, side effects have been reported with epidural analgesia such as postoperative nausea and vomiting (PONV), respiratory depression and arterial hypotension. Although the incidence of these side effects is lower than those reported with the use of systemic opioids, they can contribute to a delay in discharging patients from PACU. Epidural analgesia is also associated with perioperative hypothermia. The incidence of cognitive dysfunction is not decreased by using postoperative epidural analgesia. Assessment of the quality of analgesia by using pain visual analogue score (VAS) at rest and with movements or on coughing remains the most preferred in PACU, although there are limitations with this measurement. Epidural failure due to technical failure or malposition of the catheter represents potential problems having direct consequence on the quality of analgesia provided. All epidural catheters have to be checked and the quality of analgesia assessed before patients are discharging from PACU to the surgical wards. With advances in pain pharmacology, multimodal interventions and adjuvants can be used safely with the intent of providing better analgesia and decreasing the side effects associated with one technique.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".