Total pain, opioids, and immune checkpoint inhibitors in the survival of patients with cancer
Bibliographic record
Abstract
Experimental and observational studies have shown that opioid analgesics may increase tumor growth, potentially reduce immunotherapy efficacy, and shorten survival. As a result of the lack of clinical data, the current rationale for continuing opioid analgesic treatment is based on animal models, which suggests that physical pain itself may potentially influence cancer growth and exert immunosuppressive effects. Total pain encompasses the various factors that patients may experience during their cancer journey: physical symptoms, social isolation/loneliness, psychological, spiritual/existential, and financial distress. These need to be screened and discussed with patients to help them cope with the treatment and disease. As each issue may affect survival, it is essential to identify them to understand how they might affect the patient's immune system, influence immunotherapy outcomes, and ultimately, survival. The question arises whether a single factor, such as the combination of opioids and immune checkpoint inhibitors, negatively affects treatment outcomes. While there is a risk of fostering opioid phobia, the complex interplay between total pain, quality of life, and the immune system must be considered. Thus, in studies that appropriately investigate the interactions between opioid analgesics and the immune system, it is essential to consider all the distress factors that patients may experience at each stage of their illness.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".