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Record W3172159066 · doi:10.1177/13524585211020221

Genes and environment in multiple sclerosis: Impact of temporal changes in the sex ratio on recurrence risks

2021· article· en· W3172159066 on OpenAlexaff
A. Dessa Sadovnick, Irene M. Yee, Maria Criscuoli, Gabriele C. DeLuca

Bibliographic record

VenueMultiple Sclerosis Journal · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicDemographic Trends and Gender Preferences
Canadian institutionsUniversity of British ColumbiaUniversity of British Columbia Hospital
Fundersnot available
KeywordsProbandMultiple sclerosisDemographyRelative riskMedicineFamily historyInternal medicineConfidence intervalGeneticsBiologyPsychiatryGene

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluate the impact of temporal increase of female to male (F:M) sex ratio for persons with multiple sclerosis (MS) on the familial risk (empiric recurrence risks or RRs) for biological relatives of affected individuals. METHODS: Detailed family histories were systematically obtained from people with MS attending the University of British Columbia Hospital MS Clinic. The study cohort was born in 1970 or more recently. Data were collected from 1 September 2015 to 31 January 2019. The study was designed to allow only one proband per family. Age-corrected RRs for biological relatives of probands were calculated based on a modification of the maximum-likelihood approach. RESULTS: Data analyses were possible for 746 unique probands (531 females; 215 males) and 19,585 of their biological relatives. RRs were temporally impacted. CONCLUSION: Both genetic sharing and environmental factors are important in determining RRs. It appears that there is an increase in MS risk due to environmental factors in later life (i.e. not shared family environment). Environmental exposures in genetically predisposed individuals might be driving the MS risk. The increase in F:M ratio of RRs for sisters/brothers of female probands over time is likely due to environmental differences.

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)0.002
metaresearch head score (Gemma)0.000
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 score0.133
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.235
GPT teacher head0.327
Teacher spread0.091 · 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

Citations7
Published2021
Admission routes1
Has abstractyes

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