Infection as a cause of multiple sclerosis
Bibliographic record
Abstract
It is difficult to think of an aetiological theory that has not been suggested to explain multiple sclerosis. Disconcertingly, however, many of the aetiological questions asked over 150 years ago are still unanswered.1 Is the disease due to a vascular defect as initially suggested by Rindfleisch in 1863, who noted a blood vessel in the centre of each plaque, or is it a defect in the glial tissue as argued by Charcot in 1868 after he viewed and drew the glial and nerve changes under his microscope? Oppenheim was certain that multiple sclerosis was caused by environmental toxins. In the middle of the 20th century interest centred around the possibility that it was an immunological disease and, more recently, a genetic disease. Perhaps the most enduring questions concern a potential infectious agent. In 1894 Pierre Marie, a former student of Charcot, argued strongly that infection was the cause of multiple sclerosis and that those who disagreed had not read his papers. He did not know the …
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.016 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".