Defining hormone replacement therapy in longitudinal studies: impact on measures of effect
Bibliographic record
Abstract
Data from a nested case-control study, designed to examine the effect of hormone replacement therapy (HRT) on colorectal cancer risk, were analyzed to determine the effect of exposure definition on the estimation of risk ratios (RR). A prescription drug plan database was used to ascertain HRT prescriptions dispensed prior to index dates to cases (n = 3059) and age-matched controls (n = 12,116). HRT exposure was defined as 'prescription' and 'tablet' counts, 'conjugated estrogen only' and a method based on proportions of minimum exposure to a number of estrogens (SUM-P3 and SUM-P12). The effect of HRT was described with reference to 'ever', <5 and > or = 5 years of HRT use. Conditional logistic regression was used to estimate ORs and 95% confidence intervals (CI). Adjusted ORs for 'ever use' of HRT ranged from 0.72 (95%CI: 0.60-0.88) to 0.86 (95%CI: 0.76-0.99); for <5 years use, from 0.70 (95%CI: 0.56-0.88) to 0.89 (95%CI: 0.78-1.01) and for >5 year of HRT use, from 0.74 (95%CI: 0.59-0.92) to 0.98 (95%CI: 0.42-2.26). Various methods used to define HRT exposure produce a range of estimated ORs that vary in magnitude similar to results reported in the literature from observational studies investigating the association between HRT and colorectal cancer.
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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 | 0.699 | 0.818 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.014 |
| Bibliometrics | 0.009 | 0.021 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.007 | 0.009 |
| 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; 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".