Bibliographic record
Abstract
A 13-year-old girl arrived at a community emergency department following a fall while snowboarding. The patient had been unwell with a low-grade fever a week before this presentation. Six hours after her fall, she experienced four separate generalized tonic-clonic seizures, each lasting less than 3 min. She was treated with lorazepam and phenytoin. A lumbar puncture revealed zero red blood cells, 36 white blood cells with 94% lymphocytes. Cerebrospinal fluid (CSF) glucose and protein levels were normal. At the tertiary care centre, the patient had a fluctuating level of consciousness. Her vital signs were stable and she was afebrile. Her physical examination revealed a drowsy girl with a generally normal examination. Her muscle tone was reduced but symmetrical. There were no focal neurological findings. Acyclovir and ceftriaxone were started empirically. Phenobarbital and phenytoin were given for seizure management. Computed tomography and magnetic resonance imaging of the head were normal. CSF cultures were negative. The patient continued to have seizures despite treatment. Continuous electroencephalogram monitoring showed obvious seizure activity, including many subclinical episodes. Multiple antiepileptic drugs were required to achieve adequate seizure control. Further investigation revealed the cause of this girl's problem.
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) | 0.002 | 0.001 |
| 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 | 0.004 | 0.003 |
| 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".