The Association of Alcohol Use and Fruit and Vegetable Consumption with Cataracts among Adults: Results from the Longitudinal Canadian National Population Health Survey (NPHS)
Bibliographic record
Abstract
Background: Cataracts are the leading cause of blindness globally, so advancing the understanding their etiology is of paramount importance for development of the preventive interventions. The findings for the association of alcohol intake and fruit and vegetable consumption with cataracts in previous literature were inconsistent.\nObjective: The first study objective was to assess whether alcohol intake increases the risk of cataracts among adults. The second study objective was to assess whether fruit and vegetable consumption reduces the risk of cataracts among adults.\nMethods: A retrospective cohort study design was used. Data were obtained from the Household, Longitudinal component of the Canadian National Population Health Survey (NPHS) (1994-2011) cohort among adults aged 40 years or older. The first study objective used data from cycle 1 (1994/1995) through cycle 9 (2010/2011). Data for the second objective were obtained from the last five cycles of this survey (2002/2003-2010/2011). Alcohol use was measured as drinks per month. Fruit and vegetable consumption was assessed as daily servings. The subjects were followed until the occurrence of a cataract, death, end of the NPHS survey (2010/2011), or loss to follow-up, whichever came first. The research questions were addressed by fitting the Cox proportional hazards regression models with the inclusion of time-varying explanatory variables.\nResults: The first study included 9,889 respondents, with 1,978 incident cataracts and an incidence rate of 19.2 per 1,000 person-years for the study population from cycle 1 to cycle 9 in NPHS. A total of 7,388 respondents who met our inclusion and exclusion criteria were identified through cycle 5 to cycle 9 for the second study, of which 1,019 developed cataracts over the follow-up period, the incidence rate was 19.7 per 1,000 person-years. After adjusting for potential confounders, the hazard ratios for alcohol intake and fruit and vegetable consumption were 1.00 (95% confidence interval [CI]: 0.95 to 1.04) and 1.04 (95% CI: 0.91 to 1.19), respectively.\nConclusion: The results suggest that alcohol use and fruit and vegetable consumption are not statistically significantly associated with the risk of cataracts.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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; 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".