Anaesthetic Techniques and Strategies: Do They Influence Oncological Outcomes?
Bibliographic record
Abstract
BACKGROUND: With the global disease burden of cancer increasing, and with at least 60% of cancer patients requiring surgery and, hence, anaesthesia over their disease course, the question of whether anaesthetic and analgesia techniques during primary cancer resection surgery might influence long term oncological outcomes assumes high priority. METHODS: We searched the available literature linking anaesthetic-analgesic techniques and strategies during tumour resection surgery to oncological outcomes and synthesised this narrative review, predominantly using studies published since 2019. Current evidence is presented around opioids, regional anaesthesia, propofol total intravenous anaesthesia (TIVA) and volatile anaesthesia, dexamethasone, dexmedetomidine, non-steroidal anti-inflammatory medications and beta-blockers. CONCLUSIONS: The research base in onco-anaesthesia is expanding. There continue to be few sufficiently powered RCTs, which are necessary to confirm a causal link between any perioperative intervention and long-term oncologic outcome. In the absence of any convincing Level 1 recommending a change in practice, long-term oncologic benefit should not be part of the decision on choice of anaesthetic technique for tumour resection surgery.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".