Determinants of Intraocular Pressure of Glaucoma Patients: A Case Study at Menelik IIReferral Hospital, Addis Ababa, Ethiopia
Bibliographic record
Abstract
The main theme of the paper is the well-known problem of glaucoma which is the main cause of blindness worldwide and is also considered a major public health issue. It is usually associated with intraocular pressure above the normal range. The normal range is considered to be 10-21mmHg. Elevated intraocular pressure is a major risk factor for the development and/or progression of glaucoma, and intraocular pressure reduction is a well-known treatment strategy for slowing the progression of the disease. The objective of this article is to identify factors/covariates which affect intraocular pressure on glaucoma patients taking into consideration various demographic, socio-economic, and clinical factors. A retrospective longitudinal cohort study was conducted; the study was based on data from all glaucoma patients who visit at least 3 times repeatedly six waves from January 2016 to December 2018 at Menelik II Referral Hospital Eye Clinic. Profile plots, univariate and multivariate linear mixed effect models were used to explore the major risk factors for the progression of intraocular pressure of a patient. The predictor variables gender (p-value=0.0218), occupation (p-value=0.0025), blood pressure (p-value, 0.0263), diabetes (p-value=0.0139), ocular problem (p-value=0.0290) and type of treatment (p-value=0.0176) found statistically significant effects on intraocular pressure of glaucoma patient. The interaction effects, i.e. time with age (p-value<.0001), time with ocular problem (p-value=0.0002), time with cataract surgery (p-value=0.0002), time with duration of treatment (p-value=0.0014) and time with type of treatment (p-value=0.0262) were found statistically significant on intraocular pressure of glaucoma patient.
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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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".