Hand Surgery Anaesthesia Innovations: Balancing Efficiency, Cost, and Comfort with WALANT, Ultrasound, and Emerging Adjuncts—A Narrative Review
Bibliographic record
Abstract
Background: Hand surgery is increasingly transitioning from hospital operating theatres to outpatient settings, requiring anaesthetic methods that are efficient, cost-effective, and patient-centred. Traditional anaesthesia, such as general anaesthesia, poses challenges including prolonged recovery and physiological stress. Novel strategies, such as Wide-Awake Local Anaesthesia No Tourniquet (WALANT), ultrasound-guided distal nerve blocks, and adjunctive approaches (vapocoolant spray, patient-controlled regional analgesia, cryoanalgesia, jet injectors), have emerged to address these limitations. This narrative review consolidates current evidence regarding the efficacy, applicability, and economic implications of these evolving anaesthesia techniques. Methods: A literature search was conducted across MEDLINE, Embase, CENTRAL, and Scopus databases up to 1 June 2025. Inclusion criteria were English-language original studies on WALANT, vapocoolant sprays, ultrasound-guided distal nerve blocks, or emerging adjunctive anaesthesia methods applicable to hand and upper limb surgery. Exclusion criteria included non-English publications and those without original clinical data. Two independent reviewers screened and selected studies, ensuring relevance and methodological quality. Results: WALANT can provide high patient satisfaction, cost savings of 70–85%, and allow for real-time functional testing during surgery. Ultrasound-guided nerve blocks provided targeted analgesia, preserved elbow function, reduced the need for sedation, and improved perioperative efficiency. Adjuncts such as vapocoolant sprays significantly decreased needle-injection discomfort, offering quick and economical analgesia for superficial procedures. Other emerging adjuncts, including patient-controlled regional anaesthesia (PCRA), cryoanalgesia, and jet injectors, offered additional patient-tailored pain management options, although with higher resource demands. Conclusions: The review highlights the transformative potential of WALANT and adjunctive techniques to enhance efficiency, patient experience, and cost-effectiveness in hand surgery. Despite clear benefits, optimal application requires tailored patient selection, clinician familiarity, and consideration of procedure-specific demands.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".