Bibliographic record
Abstract
The editors and Karger Publishers would like to thank the following reviewers for their ongoing support in reviewing manuscripts for Case Reports in Neurology:Abdullah, Rawalpindi, PakistanEce Akbayir, Istanbul, TurkeyAdrià Arboix, Barcelona, SpainJavier Arpa, Madrid, SpainSteven K. Baker, Hamilton, ON, CanadaBiswajit Banik, Upper Keys, FL, USAMiguel V. Baptista, Lisbon, PortugalCan E. Bekircan-Kurt, Ankara, TurkeyPeter Berlit, Berlin, GermanySusanne Buechner, Bolzano, ItalyAlessandro Bufi, Perugia, ItalyJean-Marc Burgunder, Bern, SwitzerlandDario Cazzoli, Luzern, SwitzerlandLoh W. Chao, Selangor, MalaysiaI-Hweii A. Chen, Tampa, FL, USAVedat Cilingir, Van, TurkeyShlomi Constantini, Tel Aviv, IsraelMarco Cosentino, Varese, ItalyAndrei I. Cucu, Suceava, RomaniaFabricio F. de Oliveira, São Paulo, BrazilKresimir Dolic, Split, Croatia HrvatskaMervyn Eadie, Brisbane, QLD, AustraliaSalvatore Facciorusso, Foggia, ItalyRobert Fekete, Valhalla, NY, USAJuan C. Garcia-Monco, Bilbao, SpainGuillermo Gonzalez Hernández, Mexico City, MexicoStefano Grazioli, Varese, ItalyDavid Grimes, Ottawa, ON, CanadaCarlos A. Hurtado González, Cali, ColumbiaEiman Y. Ibrahim, Bridgeport, CT, USAKazunari Ishii, Osaka, JapanMehmet Kantar, Izmir, TurkeyErica Kornblith, San Francisco, CA, USAMrinmoy Kundu, Bhubaneswar, IndiaMaciej Kurpisz, Poznan, PolandKaren G. Langer, New York, NY, USASteven R. Levine, New York, NY, USATomoo Mano, Kashihara, JapanCintia Marchesoni, Buenos Aires, ArgentinaAlbee Messing, Madison, WI, USAAnne Millerand, Paris, FranceTakahiro Mitsueda, Osaka, JapanAbhay Moghekar, Baltimore, MD, USAJaime D. Mondragon, Groningen, The NetherlandsNicola Montemurro, Pisa, ItalySaravanan Narayanamoorthi, Altrincham, UKPrashant A. Natteru, La Crosse, WI, USAHuong B.T. Nguyen, Ho Chi Minh, VietnamKazuyuki Noda, Izunokuni, JapanToshiho Ohtsuki, Kyoto, JapanAkihiro Okada, Hyogo, JapanMatthildi Papathanasiou, Lyon, FranceFrancesco Pasini, Milan, ItalySean P. Polster, Chicago, IL, USAAthi Ponnusamy, Salford, UKTaimoon Rasheed, Rawalpindi, PakistanRaymond L. Rosales, Manila, PhilippinesJonathan Roth, Tel Aviv, IsraelClaudio Sacks, Viña del Mar, ChileMonica Saini, Singapore, SingaporeGian-Pietro Sechi, Sassari, ItalyJohann Sellner, Mistelbach, AustriaFederica N. Sepe, Pavia, ItalyAung Sett, Lancaster, UKYuko Shimizu, Tokyo, JapanAndreas Steck, Basel, SwitzerlandYasuyuki Takai, Tokyo, JapanKay S. Tan, Kuala Lumpur, MalaysiaTatsuya Tanaka, Narita, JapanAbhilash Thatikala, Memphis, TN, USAHiroki Toda, Osaka, JapanMarco Trucco, Loano, ItalyValentina Tudisco, Messina, ItalyGiacomo Urbinati, Bologna, ItalyMarjo van der Knaap, Amsterdam, The NetherlandsJuan Vilchez Padilla, Valencia, SpainElvan Wiyarta, Jakarta, IndonesiaTian-Ci Yang, Xiamen, ChinaBulent Yazici, Bursa, TurkeyDonald H.H. Yeo, Singapore, SingaporeAtilla Yilmaz, Istanbul, TurkeyFumihito Yoshii, Takamura, JapanBryan B. Young, Owen Sound, ON, CanadaReza Zahedpasha, Gorgan, Iran
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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 | 0.014 | 0.178 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.300 | 0.194 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".