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Record W4400112991 · doi:10.1111/ene.16337

Oral Presentations

2024· article· en· W4400112991 on OpenAlexaboutno aff

Bibliographic record

VenueEuropean Journal of Neurology · 2024
Typearticle
Languageen
FieldMedicine
TopicOsteomyelitis and Bone Disorders Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMedical physics

Abstract

fetched live from OpenAlex

Background and aims: Active smoking is a known risk factor for Multiple sclerosis (MS) development and poor prognosis. However, the impact of past exposure to parental smoking (ParS), including maternal smoking during pregnancy (MSDP) is not well defined. We aimed to investigate how these types of early age exposures affect MS risk among adults. Methods: Using the data collected by the EnvIMS study, a large multinational case–control population-based study, we investigated the association between MS and smoking habit, MSDP and maternal/paternal smoking (MaS, PaS) in Canadian, Italian, and Norwegian populations. Data were collected with EnvIMS-Q, designed to investigate environmental exposures during early life stages. Adjusted odds ratios (aOR) for index age and participants' smoking status are presented with 95% confidence intervals (95% CI). Results: We included 1565 Canadian, 2040 Italian, and 2674 Norwegian subjects. An association between MS and MSDP and MaS was observed among Norwegians: aOR 1.38 (1.12, 1.71) and 1.39 (1.17, 1.65), respectively. A tendency for PaS to be associated with MS was found among Canadians: aOR 1.21 (0.97, 1.51). No significant association to ParS (any) was detected in the Italian population. Conclusion: Selective exposure to ParS at early age may differentially increase MS risk in the general population and independently from the subject's past/current smoking habit. The developmental origin of health and disease (‘DOHaD’) theory may help interpret these findings. The absence of an association between MS and past exposure to ParS in other populations may reflect its smaller effect on MS risk compared to other factors.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.487
Threshold uncertainty score0.411

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.043
GPT teacher head0.346
Teacher spread0.303 · 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 designNot applicable
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

Citations2
Published2024
Admission routes1
Has abstractyes

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