Pathogenesis of Multiple Sclerosis: Genetic, Environmental, and Random Mechanisms
Bibliographic record
Abstract
Abstract BACKGROUND MS-pathogenesis requires both genetic factors and environmental events. The question remains, however, whether these factors and events completely describe the MS disease-process. This question was addressed using the Canadian MS-data, which includes 29,478 individuals, representing 65-83% of all Canadian MS-patients. METHODS The “ genetically-susceptible ” subset of the population, ( G ), includes everyone who has any non-zero life-time chance of developing MS, under some environmental-conditions. A “ sufficient ” environmental-exposure, for any “ genetically-susceptible ” individual, includes every set of environmental conditions, each of which is sufficient , by itself, to cause MS in that person. This analysis incorporates several different epidemiologic-parameters , involved in MS-pathogenesis, only some of which are directly-observable, and establishes “ plausible-value-ranges” for each parameter. Those parameter-value combinations (solutions) that fall within these plausible-ranges are then determined. RESULTS Only a fraction of the population can possibly be “ genetically-susceptible ”. Thus, many individuals have no possibility of developing MS under any environmental conditions. Moreover, some “ genetically-susceptible ” individuals, despite their experiencing a “ sufficient ” environmental-exposure, will never develop disease. CONCLUSIONS This analysis explicitly includes all of those genetic factors and environmental events (including interactions), which are necessary for MS-pathogenesis, regardless of whether these are known, suspected, or as yet unrecognized. Nevertheless, in addition, “ true ” randomness seems to play a critical role in disease-pathogenesis. This observation provides empirical evidence that undermines the widely-held deterministic view of nature. Moreover, both sexes seem to have a similar genetic and environmental disease-basis. If so, this indicates that this random element is primarily responsible for the currently-observed differences in disease-expression between susceptible-women and susceptible-men .
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".