The effect of medications which cause inflammation of the gastro‐oesophageal tract on cancer risk: a nested case–control study of routine Scottish data
Bibliographic record
Abstract
Bisphosphonate, tetracycline and spironolactone use has been shown to increase gastro-oesophageal inflammation, an accepted risk factor for cancer. However, evidence of the effect of these medications on gastro-oesophageal cancer risk are mixed or missing entirely. Therefore, we conducted a nested case-control study using the Primary Care Clinical Information Unit Research (PCCIUR) database from Scotland. Cases with oesophageal or gastric cancer between 1999 and 2011 were matched to up to five controls based on age, gender, year of diagnosis and general practice. Medication use was ascertained using electronic prescribing records. Conditional logistic regression was used to calculate odds ratios (ORs) for the association between medication use and cancer risk after adjustment for comorbidities and other medication use. A similar proportion of gastro-oesophageal cancer cases received bisphosphonates (3.9% vs. 3.5%), tetracycline (6.0% vs. 6.0%) and spironolactone (1.4% vs. 1.1%) compared with the controls. The adjusted ORs for the association between gastro-oesophageal cancer and bisphosphonates, tetracycline and spironolactone were 1.05 (95% CI: 0.85, 1.31), 0.99 (95% CI: 0.84, 1.17) and 1.04 (95% CI: 0.73, 1.49). Further analysis revealed bisphosphonates were associated with increased oesophageal cancer risk (1.34, 95% CI: 1.03, 1.74) but reduced gastric cancer risk (0.71, 95% CI: 0.49, 1.03), although there was no obvious dose-response relationship. Overall, there is little evidence that the use of bisphosphonate, tetracycline or spironolactone is associated with increased risk of gastro-oesophageal cancer. Our findings should reassure GPs and patients that these widely-used medications are safe with respect to gastro-oesophageal cancer risk.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".