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Record W2102536646 · doi:10.1186/1740-2557-2-9

Co-occurrence of autoimmune thyroid disease in a multiple sclerosis cohort

2005· article· en· W2102536646 on OpenAlexaff
J Scott Sloka, Pryse-WEM Phillips, Mark Stefanelli, Carol Joyce

Bibliographic record

VenueJournal of Autoimmune Diseases · 2005
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMedicineGraves' diseaseMultiple sclerosisDiseaseThyroid diseasePopulationCohortThyroidThyroiditisInternal medicinePediatricsAutoimmune diseaseImmunologyEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: Multiple sclerosis (MS), Hashimoto's disease and Graves' disease are autoimmune diseases that may share similar pathogenic mechanisms. The co-occurrence rates and demographic characteristics of Graves' disease and Hashimoto's disease (HT) in our MS population are compared with the general population. METHODS: The prevalence of thyroid disease in our MS patients was determined by chart review and survey. Previous diagnosis of thyroid disease, age at diagnosis, treatment used, and about the use of disease modifying medications used to treat their MS were asked. Chart reviews were used to estimate the population prevalence of Graves' disease and Hashimoto's disease and to estimate the demographics of patients with thyroid disease. RESULTS: A significant co-occurrence of Graves' disease with MS (p = 0.002), and a non-significant co-occurrence of Hashimoto's disease were noted (p = 0.097). No difference in the age of onset or gender of thyroid disease in MS patients compared to the general population was found. CONCLUSION: There is a significant co-occurrence in patients with MS and Graves' disease, and a trend to co-occurrence in patients with MS and Hashimoto's disease. There are no differences in the demographics of patients with thyroid disease in our MS patients compared to the general population.

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)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.049
GPT teacher head0.316
Teacher spread0.268 · 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.

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

Citations72
Published2005
Admission routes1
Has abstractyes

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