MétaCan
Menu
Back to cohort
Record W1778641348 · doi:10.1111/ane.12520

Restless legs syndrome and pregnancy: prevalence, possible pathophysiological mechanisms and treatment

2015· review· en· W1778641348 on OpenAlexaff
Ravi Gupta, Mohan Dhyani, Tetyana Kendzerska, Seithikurippu R. Pandi‐Perumal, Ahmed S. BaHammam, Prachaya Srivanitchapoom, Sanjay Pandey, Mark Hallett

Bibliographic record

VenueActa Neurologica Scandinavica · 2015
Typereview
Languageen
FieldMedicine
TopicRestless Legs Syndrome Research
Canadian institutionsInstitute for Clinical Evaluative SciencesSunnybrook Health Science Centre
FundersNational Institutes of Health
KeywordsRestless legs syndromePathophysiologyMedicinePregnancyPediatricsPsychiatryInternal medicineNeurologyBiology

Abstract

fetched live from OpenAlex

Restless legs syndrome (RLS) is a common sleep disorder that may be associated with pregnancy. Studies have found that the prevalence of RLS among pregnant women ranged from 10 to 34%. Typically, there is complete remission of symptoms soon after parturition; however, in some patients, they may continue postpartum. RLS has been shown to be associated with a number of complications in pregnancy including preeclampsia and increased incidence of Cesarean sections. Although multiple hypotheses have been proposed to explain this association, each individual hypothesis cannot completely explain the whole pathogenesis. Present understanding suggests that a strong family history, low serum iron and ferritin level, and high estrogen level during pregnancy might play important roles. Vitamin D deficiency and calcium metabolism may also play a role. Medical treatment of RLS during pregnancy is difficult and challenging considering the risks to mother and fetus. However, in some cases, the disease may be severe enough to require treatment.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.003
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.125
GPT teacher head0.380
Teacher spread0.255 · 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 designNot applicable
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

Citations122
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueActa Neurologica ScandinavicaSame topicRestless Legs Syndrome ResearchFrench-language works237,207