A Computed Tomography Scan Near Miss of an Intraorbital Wooden Foreign Body
Bibliographic record
Abstract
When intraorbital wooden foreign bodies are missed, the consequences can be devastating. While the gold standard diagnostic imaging is computed tomography (CT), it has low sensitivity. We present a 61-year-old man with a bamboo injury to his right eye. He underwent two CT scans that failed to raise the possibility of intraorbital foreign bodies. Upon additional review, a rectangular-shaped pocket of air was identified in the orbit which was most consistent with wooden foreign bodies based on the clinical history. A combined mid-lid approach followed by a transconjunctival and transcaruncular extension were employed to remove several wooden splinters. Postoperatively, due to recurrent orbital compartment syndrome, he required a second decompression with an inferior rim osteotomy. He had good recovery at 3 months follow-up. Overall, intraorbital wooden foreign bodies are challenging to diagnose due to imaging limitations. Providing a clear history and suspected diagnosis to radiology is critical for diagnosis.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".