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Record W2064135127 · doi:10.1097/jnn.0b013e3181b6be96

The Impact of Pain and Other Symptoms on Quality of Life in Women With Relapsing-Remitting Multiple Sclerosis

2009· article· en· W2064135127 on OpenAlexfundno aff
Pamela Newland, Robert T. Naismith, Margaret Ullione

Bibliographic record

VenueJournal of Neuroscience Nursing · 2009
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsnot available
FundersNational Institutes of HealthNational Institute of Neurological Disorders and StrokeSigma Theta Tau InternationalMcGill UniversityUniversity of Missouri
KeywordsDepression (economics)MedicineQuality of life (healthcare)Multiple sclerosisSleep disorderPhysical therapySleep (system call)InsomniaPsychiatry

Abstract

fetched live from OpenAlex

The purpose of this study was to assess pain, fatigue, depression, sleep disturbance, and quality of life (QOL) in women with relapsing-remitting multiple sclerosis (RRMS) compared with healthy controls. A prospective, cross-sectional, matched-control study was conducted in women with RRMS compared with healthy women. Compared with healthy women, women with RRMS had (a) greater pain presence over 7 days (67%), (b) higher pain intensity, and (c) more pain interference. Pain had a negative impact on fatigue, depression, and sleep in both groups. In all participants, fatigue, depression, and sleep disturbance contributed to decreased mental QOL (mental component summary of QOL scores). Pain has significant nursing implications for women with RRMS. Pain often occurs in association with fatigue, depression, and sleep disturbance, which can lead to a decreased mental QOL.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.100
GPT teacher head0.376
Teacher spread0.277 · 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
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

Citations36
Published2009
Admission routes1
Has abstractyes

Explore more

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