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Record W2085852026 · doi:10.1586/14737175.2014.927734

Differentiating nocturnal leg cramps and restless legs syndrome

2014· review· en· W2085852026 on OpenAlexaff
Abdul Qayyum Rana, Fatima Khan, Abdullah Mosabbir, William G. Ondo

Bibliographic record

VenueExpert Review of Neurotherapeutics · 2014
Typereview
Languageen
FieldMedicine
TopicRestless Legs Syndrome Research
Canadian institutionsUniversity of TorontoParkinson's Clinic of Eastern Toronto & Movement Disorders Centre
Fundersnot available
KeywordsRestless legs syndromeMedicineEveningMuscle crampPhysical therapyPhysical medicine and rehabilitationNocturnalAnesthesiaInternal medicineInsomniaPsychiatry

Abstract

fetched live from OpenAlex

Leg pain and discomfort are common complaints in any primary physician's clinic. Two common causes of pain or discomfort in legs are nocturnal leg cramps (NLC) and restless leg syndrome (RLS). NLC present as painful and sudden contractions mostly in part of the calf. Diagnosis of NLC is mainly clinical and sometimes involves investigations to rule out other mimics. RLS is a condition characterized by the discomfort or urge to move the lower limbs, which occurs at rest or in the evening/night. The similarity of RLS and leg cramps poses the issue of errors in diagnosing and differentiating the two. In this paper we review the pathopysiology of each entity and their diagnosis as well as treatment. The two conditions are then compared to appreciate the differences and similarities. Finally, suggestions are recommended for complete assessment.

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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
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 head0.105
GPT teacher head0.460
Teacher spread0.355 · 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

Citations10
Published2014
Admission routes1
Has abstractyes

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