A teenager with ocular signs after tongue injury
Bibliographic record
Abstract
A 16-year-old female presented to the Emergency Department with a tongue laceration. She was playfighting with her brother who swung a samurai sword and cut her tongue as she tried to dodge it. She denied headaches, constitutional symptoms, weakness, or loss of consciousness. The history was otherwise unremarkable. She was alert with normal vital signs. Her tongue was swollen with a 1-cm laceration. She was admitted due to inability to tolerate oral intake. During admission, the patient reported having difficulty opening her right eye. On exam, she was found to have anisocoria but it was unclear whether this finding was new (Figure 1). With no other symptoms, her difficulty with right eye opening was presumed to be from direct trauma and the patient was discharged home. She returned days later with persistent symptoms. On examination, she was found to have right-sided ptosis and anisocoria. Pupils were 3 mm on the right and 5 mm on the left in bright illumination, with the right pupil dilating poorly and slowly in dim illumination. Her right neck was tender with no cervical spine tenderness. Carotid pulses were symmetric with no bruits. The remainder of the examination was unremarkable. Urgent imaging assisted in the 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".