Examining Incidence of Acute Angle Closure Glaucoma in U.S. Emergency Departments
Bibliographic record
Abstract
Introduction: Acute angle closure glaucoma (AACG) is a medical emergency that presents with blurred vision, painful red eye, headache, nausea, and vomiting. AACG presents from a rapid rise in the intraocular pressure in the eye most common due to a sudden blockage in the drainage of aqueous fluid. Strong risk factors associated with AACG include female gender, hyperopic vision, previous AACG, advanced age and Inuit and Asian ethnicity. The purpose of this study was to identify additional factors that contribute to AACG. Methods: The 2015 Nationwide Emergency Department Sample (NEDS) was used to model the incidence of AACG in U.S. emergency departments based on various patient demographic and socioeconomic data. There were 5,241 weighted cases (0.0046%) of acute angle closure glaucoma in the 2015 NED dataset. Results: Results of multivariate logistic regression indicate that older patients, particularly patients 65-84, were significantly more likely to present to the ED with acute angle closure glaucoma (OR: 21.036, CI: 3.104;142.547). Males were significantly less likely to present with acute angle closure glaucoma than females (OR: 0.614, CI: 0.461; 0.817). There were no significant differences based on payer or median household income. While no significant differences were found based on the presence of the comorbidity heart failure, patients without hypertension or diabetes were significantly more likely to experience acute angle closure glaucoma than patients with hypertension or diabetes. Conclusions The results of this study provide further evidence on the risk factors that contribute to AACG. Interestingly, those with hypertension or diabetes were less likely to experience AACG. A potential protective mechanism of these comorbidities can be explored further in future analyses.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".