The Prevalence of Eyesight Deterioration in People Aged Over 50 Years and Its Correlation With Type II Diabetes in Trinidad
Bibliographic record
Abstract
Background: The aim of the study was to determine the prevalence of eyesight deterioration and its correlation with type II diabetes in people aged 50 years and above. In addition, this study specifically focuses on assessing the relationships between demographics, various eye conditions and regression with respect to type II diabetes. Methods: This was a retrospective study which comprised 268 patients with eyesight problems. These participants were selected from the ophthalmology and diabetic clinics of two major health authorities in Trinidad. Both males and females over the age of 50 years of different ethnic groups with a history of eyesight problems or form of eyesight deterioration and/or type II diabetes were included in this study. Random stratified sampling was utilized to obtain samples from both hospitals. Data collection was done via questionnaires. Results: Data of our study showed that the people affected with eye problems were in the age group of 60 - 75 years. Of the study participants, 59.3% were affected with cataract followed by glaucoma (19.4%). Data also showed that 181 were diabetic and affected with one or the other eye problem. There was a correlation between incidence of eyesight deterioration and type II diabetes in people aged over 50 years. Conclusion: This study determined that there is, to an extent, a correlation between the incidence of eyesight deterioration and type II diabetes in people aged over 50 years. J Endocrinol Metab. 2019;9(1-2):29-32 doi: https://doi.org/10.14740/jem553
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".