Research in Perioperative Care of the Cancer Patient: Opportunities and Challenges
Bibliographic record
Abstract
The theory that the perioperative period is critical for oncological outcomes has been a matter of extensive preclinical and clinical research. Basic science research strongly supports the notion that surgical stress, anesthetics, and analgesics influence the mechanisms of cancer progression. Hence, it is hypothesized that perioperative interventions that impact mechanisms or predictors of tumor progression can also affect patients' survival. As a result of that hypothesis, clinical researchers have conducted many retrospective studies. However, much fewer randomized controlled trials have been performed to investigate whether surgery itself (minimally invasive versus open procedures), anesthetics (volatile anesthetics versus propofol-based anesthesia), analgesics (opioids versus opioid-free anesthesia), and blood transfusions (transfusions versus no transfusions) modify the survival of patients with cancer. Unfortunately, randomized controlled trials have failed to translate the preclinical results into clinical outcomes. In this review, I will highlight the challenges of translating basic science to clinical outcomes. We will also point out opportunities for future research.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".