Characteristics of Traumatic Optic Neuropathy (TON) Cases Diagnosed at Cicendo Eye Hospital in 2022-2024
Bibliographic record
Abstract
Introduction: Traumatic Optic Neuropathy (TON) is uncommon, with an overall incidence of 0,7-2,5%, and yet significantly vision threatening. Characteristics of TON cases are of great aid in basing diagnosis, research, and therapy, and are documented in lengths in developed nations such as Canada and UK. This study aims to paint a picture of TON cases in Indonesia, especially Cicendo Eye Hospital. Methods: Descriptive data are retrospectively collected from Electronic Medical Record (EMR) of Cicendo Eye Hospital. The data is collected from Diagnosed cases of TON from 2022-2024, amounting to 147 cases. Result: There are 147 diagnosed cases of TON in Cicendo eye hospital from 2022-2024, age of TON patients averages 31,7 ± 16.0, with 79,6% are of male gender, etiology of TON are traffic related trauma (61.8%), occupation related (15.3%), violence related (4.1%), and others (22.9%). Most cases (88.4%) received therapy after 24 hours of onset of trauma. Cases have varied initial visual acuity, ranging from no light perception (18.4%), light perception (8.2%), hand movement (17.7%), finger counting (21.1%), and better(20.4%). Management of TON includes oral citicoline alone(60.5%), with oral corticosteroid (18.4%), or with intravenous corticosteroid (21.1%). Conclusion: Within the 147 TON cases in Cicendo eye hospital from 2022-2024, TON patients are of age 31,7 ± 16.0, mostly male (79.6%), related to traffic accident (61.8%), evaluated 24 hours after onset (88.4%), have visual acuity for finger counting (21.1%), and treated with oral citicoline alone (60.5%).
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".