Opioid use and the risk of cancer incidence and mortality: a systematic review
Bibliographic record
Abstract
There is abundant but discrepant scientific literature reporting an effect of opioids on the course of cancer. The International Agency for Research on Cancer monographs recently classified opium consumption as carcinogenic to humans in certain organs, raising concerns this may be due at least in part to the alkaloids opium contains (such as morphine and codeine). This systematic review investigated whether opioid exposure among cancer-free individuals is independently associated with the risk of future cancer incidence or cancer mortality. An electronic database search was conducted in PubMed, EMBASE, Web of Science, PsycINFO, International Pharmaceutical Abstracts, CINAHL and Scopus. Studies were included if they provided a statistical estimate of cancer mortality, cancer incidence, or cancer risk following opioid exposure. Study quality was assessed using the Newcastle-Ottawa Scale. Study characteristics and outcomes were extracted and analysed in a descriptive narrative synthesis. There were 27 studies that met the inclusion criteria, representing a total of 4,542,745 participants. Twelve of the 27 were rated as high quality according to the Newcastle-Ottawa Scale. The observed data is consistent with a small increase in the risk of cancer incidence or cancer mortality following opioid exposure, particularly in a subset of organs. There is, however, considerable uncertainty in the evidence given the substantial risk of bias in estimating the overall effect of opioid exposure on cancer outcomes in these studies. This review synthesises studies reporting cancer risk following opioid exposure and identifies the key methodological factors influencing ongoing uncertainty estimating the true effect. Rigorous epidemiological studies employing specific methods to minimize bias are warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".