Post Traumatic Endophthalmitis: Incidence and Risk Factors
Bibliographic record
Abstract
Post traumatic endophthalmitis is an uncommon but severe complication of ocular trauma. We aimed to identify the incidence of post traumatic endophthalmitis and its contributing risk factors in Feiz hospital (Isfahan, Iran) from 2006 until 2010. Medical records of 1042 patients with open globe injury were analyzed and data were collected including age, sex, location of being injured, visual acuity (VA), time from injury to hospitalization and to repair, site of ophthalmic injury and the presence of foreign body. The frequency of post-traumatic endophthalmitis was about 2.1% (N = 22) of all patients. Nine of 22 cases with endophthalmitis were under 8 years. The visual acuity at the time of admission was seen to be contributed to high rate of endophthalmitis. Intraocular foreign body was detected in 139 patients; and the rate of endophthalmitis was 5% among these patients. Statistical analysis showed significant relationship between presence of foreign body and higher rate of endophthalmitis. Also, duration of hospitalization was significantly different between two study groups (P = 0.019). There were no significant differences between two groups in terms of other studied variables. Patients with low age, low visual acuity at admission, presence of intraocular foreign body and long duration of hospital stay had a higher risk of endophthalmitis after the repair of the globe. Compared to the reports of other large institutions, we can attribute the low incidence rate of endophthalmitis in our institution to the early use of systemic antibiotics such as gentamycin and cephalosporins in the first hour of hospitalization until discharge.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".