The association between hormonal contraceptive use and glaucoma in women of reproductive age
Bibliographic record
Abstract
AIMS: We aimed to investigate the association between hormonal contraceptive (HC) use and the incidence of glaucoma in females of reproductive age with a focus on duration and type of HCs used. METHODS: A retrospective cohort study with a case-control analysis (nested case-control) was undertaken using data from IQVIA's electronic medical record (IQVIA, USA) from 2008 to 2018. Within a cohort of 4 871 504 women, cases of glaucoma or ocular hypertension were identified. Subjects were followed to the first diagnosis of glaucoma. Each glaucoma case was matched to four controls by age, body mass index and follow up time. The main outcome measure was the first diagnosis of glaucoma defined by the first ICD-9/10 code for glaucoma or ocular hypertension. RESULTS: Among 4 871 504 women identified, there were 2366 cases of glaucoma and 9464 controls. Regular users of hormonal contraceptives had an elevated risk of glaucoma compared to non-users with an adjusted incident rate ratio (aIRR) of 1.57 (95% CI: 1.29-1.92). Current users were of greatest risk (aIRR of 2.38, 95% CI: 1.81-3.13), whereas the aIRR among past users was 1.08 (95% CI: 0.82-1.43). The aIRR for glaucoma increased from 0.82 (95% CI: 0.70-0.95) among those with one or two prescriptions in the 2 years prior to the first diagnosis of glaucoma to 1.54 (95% CI: 1.32-1.81) among those with greater than four prescriptions. CONCLUSIONS: This nested case-control study demonstrated an elevated risk, albeit low, of glaucoma in females of reproductive age who use regular hormonal contraception. Future studies are needed to confirm these findings.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".