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Record W2274742391 · doi:10.1177/1352458515588582

In multiple sclerosis anxiety, not depression, is related to gender

2015· article· en· W2274742391 on OpenAlexaff
Marie Théaudin, Kristoffer Romero, Anthony Feinstein

Bibliographic record

VenueMultiple Sclerosis Journal · 2015
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsSunnybrook HospitalHealth Sciences CentreSunnybrook Health Science CentreSt. Michael's Hospital
Fundersnot available
KeywordsAnxietyDepression (economics)Hospital Anxiety and Depression ScaleMultiple sclerosisPopulationEtiologyPsychiatryPsychologyClinical psychologyMedicine

Abstract

fetched live from OpenAlex

BACKGROUND: There is a high prevalence of depressive and anxiety disorders in multiple sclerosis (MS), a disease 2.5 times more frequent in females. Contrary to the general population, in whom studies have demonstrated higher rates of depression and anxiety in females, little is known about the impact of gender on psychiatric sequelae in MS patients. OBJECTIVES: We conducted a retrospective study to try to clarify this uncertainty. METHODS: Demographic, illness-related and behavioral variables were obtained from a neuropsychiatric database of 896 patients with a confirmed diagnosis of MS. Symptoms of depression and anxiety were obtained with the Hospital Anxiety and Depression Scale (HADS). Gender comparisons were undertaken and predictors of depression and anxiety sought with a linear regression analysis. RESULTS: HADS data were available for 711 of 896 (79.35%) patients. Notable gender differences included a higher frequency of primary progressive MS in males (p = 0.002), higher HADS anxiety scores in females (p < 0.001), but no differences in HADS depression scores. CONCLUSION: In MS, gender influences the frequency of anxiety only. This suggests that the etiological factors underpinning anxiety and depression in MS are not only different from one another, but also in the case of depression, different from those observed in general population samples.

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)0.003
metaresearch head score (Gemma)0.005
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 score0.236
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.242
GPT teacher head0.340
Teacher spread0.098 · 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

Citations47
Published2015
Admission routes1
Has abstractyes

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