Glucosamine and Cancer Incidence in Osteoarthritis: A Prevalent New‐User Cohort Design
Bibliographic record
Abstract
OBJECTIVE: Observational studies have associated glucosamine, used to treat joint pain and osteoarthritis, with reductions in cancer incidence, although their study design was affected by selection bias. We assessed this association using a study design that mitigates this selection bias. METHODS: We used the UK Clinical Practice Research Datalink to identify a cohort of patients diagnosed with osteoarthritis during 1995 through 2017. The prevalent new-user cohort design was employed to match glucosamine initiators with non-users on time-conditional propensity scores, who were observed until cancer incidence. Hazard ratios (HRs) and 95% confidence intervals (CIs) of cancer incidence were estimated to compare glucosamine initiators with non-users. RESULTS: The study cohort of patients with osteoarthritis included 20,541 glucosamine initiators who were matched to 20,541 non-users. Over an average follow-up of eight years, the overall incidence rate of any cancer was 16.4 per 1,000 per year. The HR of any cancer incidence with glucosamine treatment was 0.97 (95% CI 0.91-1.02) compared with non-users. For lung cancer, the HR with glucosamine treatment was 0.99 (95% CI 0.83-1.18), whereas it was 1.11 (95% CI 0.93-1.33) for colorectal cancer, 1.07 (95% CI 0.93-1.23) for breast cancer in women, and 1.03 (95% CI 0.88-1.22) for prostate cancer. CONCLUSION: In this large, real-world study of patients with osteoarthritis, designed to emulate a trial, treatment with glucosamine did not reduce the incidence of cancer. This finding reinforces that previous studies, not based on glucosamine initiators, were affected by selection bias. Our study does not support the prescription of glucosamine to prevent cancer in patients with osteoarthritis.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".