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Record W2319179200 · doi:10.1017/s0317167100010490

A Survey on the Impact of the Menstrual Cycle on Movement Disorders Severity

2010· article· en· W2319179200 on OpenAlexaffvenue
Anna Castrioto, Sara Hulliger, Yu‐Yan Poon, Anthony E. Lang, Elena Moro

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2010
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsToronto Western HospitalCentre for Movement DisordersUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMenstrual cycleMovement disordersMedicinePhysical medicine and rehabilitationPsychologyPhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: A possible influence of estrogens on the dopaminergic system has been hypothesized and investigated by several studies, and fluctuations in motor symptoms related to the menstrual cycle have been reported in some movement disorders patients. We designed a survey to quantify how frequently female patients with various movement disorders are affected by this phenomenon and its impact on symptom severity. METHODS: A questionnaire was sent to 104 women between 18- and 60-years-old diagnosed with movement disorders and regularly followed at our centre. RESULTS: From a total of 65 subjects who completed the questionnaire, 54 women reported the onset of their movement disorders before menopause. Twenty of them (37%) experienced changes in their movement disorders during the menstrual cycle. In particular, there was a significant worsening of symptom severity before (p=0.0005) and during menses (p=0.0004). CONCLUSIONS: The possible role of such changes should be taken into account when evaluating the efficacy of various therapeutic interventions in movement disorder patients.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.031
GPT teacher head0.287
Teacher spread0.256 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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

Citations14
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques→Same topicParkinson's Disease Mechanisms and Treatments→French-language works237,207→