The Multiple Sclerosis Severity Score (MSSS) re-examined: EDSS rank stability in the MSBase dataset increases 5 years after onset of multiple sclerosis
Bibliographic record
Abstract
1Royal Melbourne Hospital, 2Monash Insititute of Health Services Research, Victoria, Australia; 3MSCentrum Nijmegen, Nijmegen, The Netherlands; 4Univeristy of Bari, Bari, Italy; 5Charles LeMoyne Hospital, Quebec, 6CHUM Hopital Notre Dame, Montreal, 7Hopital Hotel-Dieu de Levis, Quebec, Canada; 8Neurological Institute, Pavia, 9Ospedale Generale Provinciale Macerata, Macerata, 10Maaslandziekenhuis, Sittard, The Netherlands; 11Kommunehospitalet, Arhus C, Denmark; 12KTU Farabi Hospital, Trabzon, Turkey; 13Hospital S. Joao, Porto, Portugal; 14Cliniques Universitaires Saint Luc, Brussels, Belgium; 15Hopital Tenon, Paris, France; 16John Hunter Hospital, New South Wales, Australia; 17Hospital Universitario Virgen Macerena, Sevilla, Spain; 18Hospital Italiano, Buenos Aires, Argentina; 19Multiple Sclerosis Centre Kamillus-Klinik, Asbach,Germany; 20FLENI, Buenos Aires, Argentina; 21Hospital Ecoville, Curibita, Brazil; 22Hospital Fernandez, Buenos Aires, Argentina; 23Assaf Harofeh Medical Center, Beer-Yaakov, Israel; 24St Vincent’s Hospital, Victoria, Australia; 25Hospital de Clinicas Jose San Martin, Buenos Aires, Argentina; 26Centro Internacional de Restauracion Neurologica, Havana, Cuba. *Equal authorship
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".