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Record W2076868088 · doi:10.1212/wnl.0b013e318229e694

Pain in neuromyelitis optica and its effect on quality of life

2011· article· en· W2076868088 on OpenAlexaff
Yoko Kanamori, Ichiro Nakashima, Yoshiki Takai, Shuhei Nishiyama, Hiroshi Kuroda, Toshiyuki Takahashi, C. Kanaoka-Suzuki, Tatsuro Misu, K. Fujihara, Yasuto Itoyama

Bibliographic record

VenueNeurology · 2011
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsCentre for Movement Disorders
Fundersnot available
KeywordsNeuromyelitis opticaMedicineQuality of life (healthcare)Multiple sclerosisBrief Pain InventoryPhysical therapyInternal medicineChronic painPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: To assess the features of pain and its impact on the health-related quality of life (HRQOL) in neuromyelitis optica (NMO). METHODS: We analyzed 37 patients with NMO or NMO spectrum disorders seen at the Department of Neurology, Tohoku University Hospital, Sendai, Japan, during the period from November 2008 to February 2009. A total of 35 of them were aquaporin-4 antibody-positive. We used Short Form Brief Pain Inventory (BPI) to assess pain and Short Form 36-item (SF-36) health survey to evaluate the HRQOL. Fifty-one patients with multiple sclerosis (MS) were also studied for comparison. RESULTS: Pain in NMO (83.8%) was far more common than in MS (47.1%). The Pain Severity Index score in BPI was significantly higher in NMO than in MS, and patients' daily life assessed by BPI was highly interfered by pain in NMO as compared with MS. Pain involving the trunk and both legs was much more frequent in NMO than in MS. SF-36 scores in NMO were lower than MS, especially in bodily pain. CONCLUSION: Our study showed that pain in NMO is more frequent and severe than in MS and that pain has a grave impact on NMO patients' daily life and HRQOL. Therapy to relieve pain is expected to improve their HRQOL.

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)0.001
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation 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 score0.061
Threshold uncertainty score0.802

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.107
GPT teacher head0.348
Teacher spread0.242 · 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.

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

Citations120
Published2011
Admission routes1
Has abstractyes

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