Bibliographic record
Abstract
A 13-year-old male presented to the emergency department (ED) with motor flinching consistent with tics—sudden, brief, purposeless repetitive movements. A video of an episode captured by his father showed that his upper body would tense while he flexed his neck forward and opened his mouth in a large single spasm, then would relax after 3 to 4 s. The episodes had been occurring for 1 month, and had significantly increased in frequency on the day of presentation. He was conscious but unable to speak during the spasms. He did not experience any weakness or loss of consciousness after the episodes. A review of systems only revealed a 1-year history of hand twitching. Personal and family history for conditions presenting with tics or movements similar to them, such as Tourette’s, acid reflux, epilepsy and psychiatric disorders were negative. On exam, he appeared well and was in no apparent distress. His head and neck, cardiovascular, respiratory and abdominal exams were normal. His neurological exam was also normal—with normal cranial nerves, muscle strength, reflexes and cerebellar testing. ED bloodwork included a venous blood gas, urinalysis, electrolytes, albumin, liver enzymes, lactate and CBC—all of which were normal. An additional test suggested the diagnosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".