Evidence Basis for Regional Anesthesia in Multidisciplinary Fast-Track Surgical Care Pathways
Bibliographic record
Abstract
Fast-track programs have been developed with the aim to reduce perioperative surgical stress and facilitate patient's recovery after surgery. Potentially, regional anesthesia and analgesia techniques may offer physiological advantages to support fast-track methodologies in different type of surgeries. The aim of this article was to identify and discuss potential advantages offerred by regional anesthesia and analgesia techniques to fast-track programs.In the first section, the impact of regional anesthesia on the main elements of fast-track surgery is addressed. In the second section, procedure-specific fast-track programs for colorectal, hernia, esophageal, cardiac, vascular, and orthopedic surgeries are presented. For each, regional anesthesia and analgesia techniques more frequently used are discussed. Furthermore, clinical studies, which included regional techniques as elements of fast-track methodologies, were identified. The impact of epidural and paravertebral blockade, spinal analgesia, peripheral nerve blocks, and new regional anesthesia techniques on main procedure-specific postoperative outcomes is discussed. Finally, in the last section, implementations required to improve the role of regional anesthesia in the context of fast-track programs are suggested, and issues not yet addressed are presented.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".