Pediatric Ocular Injuries: A 3-Year Follow-up Study of Patients Presenting to a Tertiary Care Clinic in Canada
Bibliographic record
Abstract
PURPOSE: To identify age groups or activities at risk for ocular injuries to provide parents, sports teams, schools, and hospitals with the appropriate tools for prevention strategies. METHODS: A retrospective chart review was conducted of all trauma-related cases from 2013 to 2015 and data were obtained with the use of an electronic medical record. All patients younger than 18 years who presented to the ophthalmology clinic with traumatic ocular injuries were included. RESULTS: A total of 409 patients met the inclusion criteria and all were included in this study. The mean age was 7.74 years. Boys were injured more frequently than girls (60.4%). Most ocular injuries occurred between the ages of 2 and 9 years (51.8%). The most common sport was soccer, followed by ball/ice hockey, which differs from previous study findings. This may highlight the increasing popularity of soccer and the risk it may entail. Injuries occurred at home in 23.2% of cases. Final visual acuity was 20/40 or better in 77% of patients. CONCLUSIONS: These findings are comparable to the authors' previous data and to those of the only other Canadian study done on this subject, with the exception of an increased incidence of soccer-related injuries in the current cohort, highlighting an area important to future prevention strategies. [J Pediatr Ophthalmol Strabismus. 2020;57(3):185-189.].
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".