Profile of eye-related emergency department visits in Ontario – a Canadian perspective
Bibliographic record
Abstract
BACKGROUND: Understanding the epidemiology of ophthalmic presentations to emergency departments can help guide resource allocation, medical education programs, and optimize the patient experience. The purpose of this investigation was to summarize and assess the urgency of ophthalmic presentations in emergency departments (EDs) in Ontario, Canada over a 5-year period. METHODS: This was a multicentered retrospective review of all patient presentations to EDs in Ontario between January 1st, 2012, to December 31st, 2017. Presentations were included if patients had an ophthalmic related ICD-10 code as their primary problem prompting ED presentation. RESULTS: A total of 774,057 patients patient presentations were included across the pediatric (149,679 patients) and adult (624,378 patients) cohorts. The mean (SD) age at presentation was 47.4 (17.9) years, and 6.54 (5.20) in the adult and pediatric cohorts respectively. Of the total presentations, 256,776 (33.1%) were due to a trauma related presentation. Problems pertaining to Cornea and External disease were the most common reason for presentation (51.0% of cases). Of all presentations, 34.1% were classified as either 'emergent' or 'likely emergent'; the remaining presentations were either 'non-emergent' (39.5%) or the urgency 'could not be determined' (26.4%). The three most frequent presentations were due to conjunctivitis (121,175 cases or 15.7%), ocular foreign bodies (104,322 cases or 13.5%), and corneal / conjunctival abrasions (94,554 cases of 12.2%). CONCLUSIONS: This investigation summarizes all ophthalmic presentations to EDs in Ontario, Canada over a 5-year period. The results of this investigation can help guide ophthalmic related knowledge translation. Additionally, these results highlight that in Canadian EDs, a significant proportion of ophthalmic presentations are nonurgent; systems level efforts to improve access for eye-related complaints to healthcare professionals outside of the ED can help facilitate improved resource allocation. As we emerge from the COVID-19 pandemic, optimising the structure of patient care access is crucial to help alleviate the pressure from overburdened EDs while effectively meeting patient healthcare needs.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 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) | 0.003 | 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".