Bibliographic record
Abstract
A 14-year-old mountain biker is brought by his parents to a local emergency care centre with a 5 cm facial laceration and a headache. He had been biking on one of the local trails alone and is a bit vague about the nature of the injury; “I must have fallen and cut myself on a rock”. He called his father on his cell phone at the time and arranged to meet him at the trailhead. He was wearing a helmet that was inspected by his father, which sustained no apparent impact. His Glasgow Coma Scale score is 15, and other than occasional yawning and apparent reduced attention, he seems fine. He is a bit tender over his right shoulder, and his jersey is dirty on this side. After cleaning and suturing the wound, he is discharged home. ... Over the next two days, he complained about a persistent headache, which felt worse when he climbed the stairs, and was not relieved with ibuprofen. He has been very irritable with his two younger siblings, finding them too loud. He called from school, complaining that his headache was markedly worse during math class, and he asked to come home, where he promptly fell asleep. His mother had him complete the symptom score of the Sport Concussion Assessment Tool 2 (SCAT2) (4), which she had downloaded from the Internet. He endorses 12/22 symptoms for a total score of 34/132. She is concerned and brings him to the evening clinic. He says he does not feel right.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.053 | 0.017 |
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".