S1-4 Keynote lecture: ‘balance at the bedside’—optimizing benefits and minimizing risk through best practices
Bibliographic record
Abstract
For those with active cancer, particularly in the advanced phases of illness, opioids are the mainstay treatment for moderate or severe chronic pain. In this context, the potential for benefit when these drugs are used appropriately usually outweighs the risks of side effects, toxicities, and the potential for abuse or addiction. Even in the context of advanced illness, however, clinicians must balance the potential for risk and benefit during opioid therapy, assess risk, and make decisions about drug selection and dosing that minimizes the likelihood of adverse outcomes. Palliative care specialists generally endorse a similar view about pain in populations with other types of advanced illness—opioids are the first-line for chronic moderate or severe pain, but again, risk and benefit must be assessed and techniques used to minimize risk. When cancer or other serious illnesses are not advanced, the potential for adverse opioid effects over longer periods of administration may shift the approach to opioid treatment, emphasizing trials of non-opioid analgesics, concurrent treatments that may reduce opioid requirements, and when opioids are used, greater caution in the selection of drugs and dosing. This approach to risk assessment and techniques to minimize risk applies to all adverse opioid effects, but the most important consideration in the U.S. and some other countries is the risk of abuse and addiction. This lecture begins with a brief discussion of the pharmacological toxicities associated with opioids and an approach to risk management that responds to side effects that are commonly recognized, such as constipation and mental clouding, and those that are less often assessed, such as neuroendocrine effects. The focus then shifts to drug abuse and addiction. The relevant phenomena are described and a stepwise approach is introduced for risk minimization. This approach is appropriately considered whenever opioids are used, including the context of advanced illness. It is a type of universal precautions based on stratifying the risk of abuse and addiction, making informed decision making about opioid selection and dosing, monitoring drug-related outcomes over time, and managing problematic behaviors if they occur.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.230 | 0.111 |
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".