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Record W2331089720

Multiple sclerosis in time and space--geographic clues to cause.

2000· article· en· W2331089720 on OpenAlexaboutno aff
John F. Kurtzke

Bibliographic record

VenuePubMed · 2000
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyShetlandMultiple sclerosisDemographyLatin AmericansSocioeconomicsEthnologyHistoryMedicinePolitical science
DOInot available

Abstract

fetched live from OpenAlex

Geographically MS describes three frequency zones. High frequency areas (prevalence 30+ per 100 000) now comprise most of Europe, Israel, Canada, northern US, southeastern Australia, New Zealand, and easternmost Russia. Medium frequency areas include southern US, most of Australia, South Africa, the southern Mediterranean basin, Russia into Siberia, the Ukraine and parts of Latin America. Prevalence rates under 5 per 100 000 are found in the rest of Asia, Africa and northern South America. Migrants from high to lower risk areas retain the MS risk of their birth place only if they are at least age 15 at migration. Those from low to high increase their risk even beyond that of the natives, with susceptibility extending from about age 11 to 45. Thus MS is ordinarily acquired in early adolescence with a lengthy latency before symptom onset. MS occurred in epidemic form in North Atlantic islands: probably in Iceland and the Shetland-Orkneys; clearly in the Faroe Islands. In the Faroes first symptom onset was in 1943, heralding the first of four successive epidemics at 13 year intervals. The disease was presumably introduced by occupying British troops during World War II, with the postwar occurrences representing later transmissions to and from consecutive cohorts of Faroese. What was transmitted is thought to be a specific, widespread, persistent infection called PMSA (the primary multiple sclerosis affection) which only rarely leads years later to clinical MS. Search for PMSA is best attempted on the Faroes where there are regions still free of MS after 50 years.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.273
Threshold uncertainty score0.576

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.065
GPT teacher head0.267
Teacher spread0.202 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations171
Published2000
Admission routes1
Has abstractyes

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