P.143 How well can neuroradiologists localize clinical signs?
Bibliographic record
Abstract
Background: A basic understanding of localization for a given set of focal neurological deficits is essential for accurate acquisition and interpretation of neuroimaging. Relying on often-limited clinical information, neuroradiologists must choose the most appropriate imaging modality and tailor a study to best identify the culprit lesion to allow for accurate interpretation. Methods: A multiple-choice quiz was designed including clinical vignettes localizing to lesions within the central (CNS) and peripheral nervous systems (PNS). The quiz was pilot-tested and refined before distribution as an electronic survey to practicing neuroradiologists and fellows within newsletters from the American Society for Neuroradiology and Canadian Neurological Sciences Federation. Results: The quiz was begun by 45 neuroradiologists and completed in its entirety by 22. Most respondents were working at urban academic/teaching hospitals(81%) in the USA(42%). The majority (90%) report no clinical neurology rotation during their training. Respondents identified a high proportion (88%) of correct answers in questions about brainstem localizations. Fewer correct answers were selected in questions describing seizure semiology (44%) or flaccid weakness (59%). Conclusions: The small size of our study limits interpretation and generalizability of the findings. Identification of a potential gap in neuroradiology education relating to localization of more complex CNS and PNS presentations merits further exploration.
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 | 0.005 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.009 |
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".