Pattern of Ocular Trauma in Patients Presenting to a Tertiary Care Hospital
Bibliographic record
Abstract
Aim: To determine the pattern of ocular trauma in patients presented to a tertiary care hospital. Study Design: A descriptive cross-sectional study. Duration and Setting of the Study: Outpatient Department of Munawwar Memorial Hospital Chakwal, Pakistan from 1 September 2020 to 31 December 2020. Methods: With informed consent, data including age, gender, occupation, and nature of the object was collected from the patients presenting with a history of ocular trauma. The visual acuity of each patient was recorded using the Snellen Visual Acuity Chart at a distance of 6 meters. Details of the anterior chamber were recorded using Slit lamp biomicroscopy while and examination of the posterior chamber was carried out using direct and indirect ophthalmoscope after dilating the pupils with 0.5% tropicamide eye drops. Data was analyzed by using SPSS V-20 (SPSS Inc. Chicago, USA). Descriptive statistics were applied for data analysis. Results: Eighty-four patients presented with ocular trauma to the OPD and emergency during study duration. The range of age was 4-70 years. The male to female ratio was almost 3:1. Blunt trauma accounted for 38(45.2%) of cases followed by foreign body 28(33%), organic trauma 11(13%), chemical injury 5(5.9%), and penetrating trauma 2 (2.3%) patients. The most common site of ocular injury was the cornea 31(36.9%) followed by eyelid 7(8.3%). More than half of the subjects had trauma from the workplace and 89% were not using protective eyewear. Conclusion: Ocular trauma more commonly occur in young male individuals. Blunt trauma was also more common than the other types of ocular trauma.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".