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Record W2139135212 · doi:10.1177/10105395070190020501

Relationship between Reproductive Exposures and Age-Related Cataract in Women

2007· article· en· W2139135212 on OpenAlexaff
Natifah Che Salleh, Mimiwati Zahari

Bibliographic record

VenueAsia Pacific Journal of Public Health · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicConnexins and lens biology
Canadian institutionsRoyal College of Physicians and Surgeons of Canada
FundersUniversiti Malaya
KeywordsMedicineLogistic regressionConfoundingEye examinationEpidemiologyPublic healthRecall biasPopulationDemographyEnvironmental healthInternal medicineOphthalmologyVisual acuity

Abstract

fetched live from OpenAlex

The objective of this study is to evaluate the relationship between reproductive exposures and age-related cataract among women. This was a hospital based case-control study. The study population included female patients, aged 50 years and above who attended the Eye clinic at the University of Malaya Medical Centre. The outcome measurement was based on ophthalmologic examination by an ophthalmologist. The data on exposure was obtained from face to face interview using a structured questionnaire. In order to reduce the recall bias, patients' medical records were used to substantiate the exposure status. Multiple logistic regression was used to assess the association of age-related cataract with exogenous estrogen usage (HRT and OCP) and duration of menses. Important confounders such as age, history of diabetes, cigarette smoking and steroids usage were controlled for in the analysis. Females with 29 years or less of endogenous estrogen exposure of, have almost three times the risk of developing age related cataract (adjusted OR 3.42: 95% CI: 1.28, 9.16), similarly among those with exposure of 30-32 years (adjusted OR 3.64: 95% CI: 1.08, 12.26). Hormone Replacement Therapy used for more than three years was found to be a protective factor of age-related cataract. There is evidence that reproductive exposure may play a role in reducing the occurrence of age-related cataract among Malaysian women.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.361

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.046
GPT teacher head0.317
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2007
Admission routes1
Has abstractyes

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