Association between antibiotic use and risk of prostate cancer
Bibliographic record
Abstract
The association between antibiotics and risk of cancer has been addressed in different studies, most of which were addressing breast cancer. The objective of this study was to assess the association between antibiotics use and risk of prostate cancer. We carried out a population-based case-control study using data from Saskatchewan Health administrative databases (Canada) between the years 1981 and 2000. Cases identified by the Saskatchewan Cancer Agency were matched to 4 controls, using incidence density sampling. The effect of dosage and timing of antibiotic use, over a minimum of 15 years before diagnosis, on prostate cancer risk was assessed. Number of prescriptions and number of tablets were used as exposure definitions. Moreover, the effect of different classes of antibiotics on prostate cancer was also studied. A total of 4,052 prostate cancer cases and 16,208 matched controls were included in this study. Antibiotics exposure (number of prescriptions) during the period of 1-15 years in the past was significantly associated with an increased risk of prostate cancer; RR = 1.69, 2.61, 2.71, and 2.83 for the 4 quartiles, respectively, p-trend = 0.0001. When number of units was taken as the exposure definition, similar results were found. We did not find any effect of the timing or class of antibiotic exposure on prostate cancer risk. We found a dose-dependent association between antibiotics exposure up to 15 years in the past and risk of prostate cancer. However, the lack of temporal trends and the absence of class specific effects suggest a noncausal relationship.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".