Bibliographic record
Abstract
<h3></h3> Opioids, such as morphine, fentanyl and oxycodone, are important to treat cancer pain. Opioids are also associated with adverse effects such as respiratory depression, constipation, nausea and sedation. There are significant variations between individuals in analgesic and adverse effects. Mechanisms underlying such differences are incompletely understood, are likely multifactorial, and include genetic and environmental contributions. Many studies have investigated common variation in candidate genes assumed to be important for the pharmacodynamics and pharmacokinetics of opioids. However, such variants, including the much studied 118AG polymorphism in the <i>OPRM1</i> gene, provide conflicting results or explain only a minor part of the total variability in effects, and can not guide clinical day-to-day practice. One explanation may be that other biological systems than those intuitively connected to opioid pharmacology influence opioid efficacy. A pooled GWA study comparing high vs. low dose opioid cancer pain patients identified variability in genes encoding other central nervous systems than those traditionally associated with opioid signaling. However, GWA studies on other populations identify other genes to influence opioid sensitivity. Opioid efficacy may also be related to dose limiting adverse effects of for instance nausea and constipation are related to gene variability. Genetic variability hindering effective opioid therapy may in some patients be related to dose limiting adverse effects. Relevant genetic predictors may also be missed because studies typically include patients with different pain etiologies. It is conceivable that genetic variability relevant for one specific pain etiology is not detected in a study including an unselected cancer pain cohort. Finally, rare variants in the <i>OPRM1</i> gene may have a pronounced effect. Effects from rare variants are difficult to demonstrate in clinical studies. Still, several rare gene variations may collectively contribute to variability in the population. This may be especially true for the minor fraction of patients who can be classified as ‘true’ opioid non- or poor responders. In conclusion, albeit current knowledge for genetic opioid variability is of limited value to guide clinical day-to-day practice multiple strategies to elucidate such relationships are promising.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".