Differences by Sex in the Workup of Children at High Risk for Multiple Sclerosis (1917)
Bibliographic record
Abstract
Wednesday, April 29April 14, 2020Free AccessDifferences by Sex in the Workup of Children at High Risk for Multiple Sclerosis (1917)Naila Makhani, Christine Lebrun-Frenay, Aksel Siva, Evangeline Wassmer, Sona Narula, Soe Mar, Jonathan Santoro, … Show All … , Nusrat Ahsan, Silvia Tenembaum, J. Nicholas Brenton, Philippe Cabre, Clarisse Carra-Dalliere, Jonathan Ciron, Jerome De Seze, Kumaran Deiva, Francoise Durand Dubief, Matilde Inglese, Megan Langille, Guillaume Mathey, Rinze F. Neuteboom, Filipe Palavra, Jean Pelletier, Daniela Pohl, Daniel Reich, Juan Ignacio Rojas, Veronika Shabanova, Eugene D. Shapiro, Robert T. Stone, Eric Thouvenot, Mar Tintore, Ugur Uygunoglu, Wendy Vargas, Sunita Venkateswaran, Helene Verhelst, Patrick Vermersch, Christina Azevedo, Orhun Kantarci, Darin T. Okuda, and Daniel Pelletier Observatoire Francophone de la Sclérose en Plaques (OFSEP) Société Francophone de la Sclérose en Plaques (SFSEP) Radiologically Isolated Syndrome Consortium (RISC) Pediatric RIS Consortium (PARIS) Show FewerAuthors Info & AffiliationsApril 14, 2020 issue94 (15_supplement)https://doi.org/10.1212/WNL.94.15_supplement.1917 Letters to the Editor
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".