Timing of endoscopic surgical decompression in traumatic optic neuropathy: a systematic review of the literature
Bibliographic record
Abstract
BACKGROUND: Traumatic optic neuropathy (TON) represents a rare but devastating complication of closed head injuries. No accepted guidelines are available for medical and surgical management algorithms. A systematic review of the literature was performed to determine the optimal timing and candidacy for endoscopic surgical intervention. METHODS: A systematic review of multiple databases was performed including Medline-Ovid, EMBASE, and PubMed. Data was extracted and patients stratified based on surgical delay from trauma (≤3 days, >3 days, ≤7 days, or >7 days) as well as preoperative and postoperative vision testing (no light perception [NLP]; light perception [LP]; hand motion [HM]; or finger counting [FC] or better). RESULTS: The literature review identified 24 studies meeting inclusion criteria. In the group of patients receiving surgery ≤3 days after the antecedent event, 57% (105/183) had visual improvement, whereas in the >7-days group 51% (145/283) of patients improved. In those with NLP preoperatively, 41% (172/411) saw improvement, whereas those with LP (89%), HM (93%), or FC (85%) fared better. CONCLUSION: The literature suggests that surgical intervention for TON is indicated despite delayed presentation, and is a better choice than no intervention at all. Patients with complete blindness on presentation (NLP) tend to have a poorer surgical outcome.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".