Bibliographic record
Abstract
Abstract Introduction It has been stated that a headache is the most common neurological symptom. Identification of secondary headache syndromes is a critical skill for neurologists and neurology residents, especially in the emergency room setting; early recognition of encephalitis, especially herpes encephalitis, is crucial. Delayed diagnosis of herpes encephalitis with delayed treatment with acyclovir leads to increased morbidity and mortality. Due to this clinical importance, and in alignment with national training objectives, we created this station as part of a seven-station OSCE delivered to adult and pediatric neurology residents. Methods This is a one-station, performance-based assessment of resident competence in assessing headache, identifying secondary headache presentation, diagnosing and managing herpes simplex encephalitis through interpretation of a lab and imaging, with complication recognition, through a case presentation. Resident OSCE station instructions, examiner marking sheet, a PowerPoint presentation, and instructors' guide are included. This is primarily a summative assessment tool. Results Twenty-four senior neurology residents (PGY 3 to 5) were examined. Using a pass threshold of 70%, all residents passed the station. Resident scores ranged from 70.6% to 96.1% with a mean of 83.1% (SD 8.20%). Discussion Residents generally did well on recognizing secondary headache presentation. Residents recognized worrisome features on CSF findings and initiated prompt treatment. Knowledge of herpes encephalitis, prognosis, and possible complications was overall solid. Residents had the most difficulty interpreting imaging findings, especially MRI diffusion weighted and ADC map. Overall, residents recognized the neuro-critical situation with impending brain herniation and managed the complication successfully.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.047 | 0.012 |
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".