Etiology of hypopyon in patients presenting acutely to the emergency eye department and characteristics of hypopyon uveitis
Bibliographic record
Abstract
OBJECTIVE: To examine the etiology of undifferentiated hypopyon presenting acutely and to better characterize hypopyon uveitis. METHODS: Patients with hypopyon were retrospectively identified from presentations to the emergency eye department between January 2015 and 2022 and also from a uveitis database of 3,925 patients seen between January 2008 and January 2022. A total of 426 episodes of hypopyon occurred in 375 eyes in 359 patients, and medical records were reviewed for each patient. RESULTS: In all, 222 hypopyon episodes were due to uveitis, and 204 were due to nonuveitic causes. The most common cause of hypopyon was HLA-B27-associated uveitis in 146 patients (34.3%). The next most common causes were infectious keratitis in 125 patients (29.3%) and endophthalmitis in 63 patients (14.8%). Compared with those presenting with nonuveitic hypopyon, patients with uveitis tended to present younger (p < 0.001), were more likely to be male (p < 0.0001), had better initial and final visual acuities (p < 0.001), and had lower intraocular pressures (p = 0.030). CONCLUSION: About half of the cases of hypopyon were secondary to uveitis, most of them being associated with HLA-B27 conditions with a good prognosis, and the other half were secondary to infectious keratitis and endophthalmitis with a poor prognosis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".