Interaction between postmenopausal hormone therapy and diabetes on cataract
Bibliographic record
Abstract
OBJECTIVE: We investigated whether postmenopausal hormone therapy (HT) use interacts with diabetes, a risk factor for several age-related eye diseases. METHODS: A cross-sectional analysis of women involved in the Canadian Longitudinal Study on Aging was performed. The random sample comprised of 15,320 community-dwelling women between ages 45 and 85 years old sampled from areas adjacent to 11 data collection centers across Canada. Information on menopausal status and HT were collected by self-report. Data on diabetes and eye disease were obtained by self-report of a physician diagnosis. Multivariable logistic regression was used. RESULTS: After adjusting for demographic, lifestyle, and health variables, a multiplicative interaction was identified such that HT use for 10 years or more was associated with a much higher odds of a report of cataract in women with type 2 diabetes (odds ratio = 2.44, 95% confidence interval 1.49, 3.99) but not in long-term HT users with no diabetes (odds ratio = 1.03, 95% confidence interval 0.87, 1.21) (interaction term P value = 0.013). HT use was not associated with glaucoma or macular degeneration. CONCLUSIONS: Long-term HT use and type 2 diabetes interact in their relationship with cataract. This novel finding should be confirmed. If confirmed, women with type 2 diabetes should be informed that long-term HT use increases their risk of cataract. : Video Summary:http://links.lww.com/MENO/A519.
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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 | 0.000 | 0.001 |
| 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) | 0.005 | 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".