Perioperative pain management in thoracic surgery: A survey of practices in Australia and New Zealand
Bibliographic record
Abstract
There are few data on current trends in pain management for thoracic surgery in Australia and New Zealand. Several new regional analgesia techniques have been introduced for these operations in the past few years. Our survey aimed to assess current practice and perceptions towards various modalities of pain management for thoracic surgery among anaesthetists in Australia and New Zealand. A 22-question electronic survey was developed and distributed in 2020 with the assistance of the Australian and New Zealand College of Anaesthetists Cardiac Thoracic Vascular and Perfusion Special Interest Group. The survey focused on four key domains-demographics, general pain management, operative technique, and postoperative approach. Of the 696 invitations, 165 complete responses were obtained, for a response rate of 24%. Most respondents reported a trend away from the historical standard of thoracic epidural analgesia, with a preference towards non-neuraxial regional analgesia techniques. If representative of anaesthetists in Australia and New Zealand more widely, this trend may result in less exposure of junior anaesthetists to the insertion and management of thoracic epidurals, potentially resulting in reduced familiarity and confidence in the technique. Furthermore, it demonstrates a notable reliance on surgically or intraoperatively placed paravertebral catheters as the primary analgesic modality, and suggests the need for future studies assessing the optimal method of catheter insertion and perioperative management. It also gives some insight into the current opinion and practice of the respondents with regard to formalised enhanced recovery after surgery pathways, acute pain services, opioid-free anaesthesia, and current medication selection.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".