Epidemiological Trends of Traumatic Optic Nerve Injuries in the Largest Canadian Adult Trauma Center
Bibliographic record
Abstract
BACKGROUND: There has been a paucity of information on the epidemiology of traumatic optic neuropathy (TON). This study documents epidemiology of TON over 2 decades in the largest level I adult trauma center in Canada. METHODS: Data on all the trauma patients admitted to Sunnybrook Health Sciences Centre from 1986 to 2007 were collected in a prospective database. The aggregate data on optic nerve injuries including demographic data, etiology, Injury Severity Score (ISS), and associated head and facial injuries were recorded. These were analyzed using univariate and multivariate techniques to summarize the association of different variables with TON. RESULTS: During the study period, 0.4% of all trauma patients had TON. The respective demographics for TON group were as follows: male, 76%; median for age, 33.5 years; length of hospital stay, 14 days; ISS, 32; and case fatality, 14%. About two thirds of patients with TON had associated significant head injuries. Conversely, 2.3% of patients with head injury had TON. The relative incidence of TON per year has remained variable from 0% to 1.2%. Motorized vehicle accidents remained the main etiology of TON (63%), but fall had the highest relative frequency leading to TON. In univariate analysis, both ISS and significant head injury were associated with TON. In multivariate analysis, TON was associated with only nasoethmoid complex fractures and significant head injury. CONCLUSIONS: These data provide useful information on the frequency and etiologies of TON. It also highlights the importance of studies on better diagnostic tools for TON.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".