Understanding the Limitations of the Odds Ratio in a Case-Control Study of the Association Between Breast Cancer and Exposure to Antipsychotic Drugs
Bibliographic record
Abstract
Evidence suggests that women with schizophrenia are less likely to be screened for breast cancer, more likely to suffer from breast cancer, and more likely to die of breast cancer than women without schizophrenia or general population controls. Antipsychotic drugs, and especially prolactin-raising antipsychotic drugs, have been suggested to increase the breast cancer risk, but the evidence has so far been inconclusive. Against this background, a recent, large, nationwide, case-control study in Finland examined the odds of previous prolonged exposure to prolactin-raising and prolactin-sparing antipsychotic drugs in women with schizophrenia who were (cases) versus were not (controls) diagnosed with breast cancer. The study found that, relative to < 1 year of antipsychotic exposure, breast cancer was associated with significantly increased odds of previous, prolonged (> 5 years) exposure to prolactin-raising antipsychotics. The associations were not statistically significant for prolactin-sparing antipsychotics. The study is critically examined from the perspective of interpretation of the odds ratio and its limitations in order to help readers understand how to better evaluate and generalize findings in case-control studies. This is necessary because results in case-control studies are often incorrectly interpreted, and the limitations of the odds ratios derived in such studies are often not recognized. It is concluded that the design and findings of the reviewed study could not allow readers to judge whether or not prolactin-sparing antipsychotics are associated with lower breast cancer risk than prolactin-raising antipsychotics. In contexts other than breast cancer risk, adverse consequences associated with prolactin elevation are well known, and avoidance or management of hyperprolactinemia is therefore desirable.
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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 | 0.554 | 0.771 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.006 | 0.008 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".