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Record W2594055181 · doi:10.7224/1537-2073.2016-058

Fatigue and Mood States in Nursing Home and Nonambulatory Home-Based Patients with Multiple Sclerosis

2017· article· en· W2594055181 on OpenAlexfundno aff
Zilfah Younus, Caila B. Vaughn, Shaik Ahmed Sanai, Katelyn Kavak, Sahil Gupta, Muhammad Nadeem, Barbara Teter, Katia Noyes, Robert Zivadinov, Keith Edwards, Patricia K. Coyle, Andrew Goodman, Bianca Weinstock‐Guttman

Bibliographic record

VenueInternational Journal of MS Care · 2017
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsnot available
FundersUniversity at BuffaloEMD SeronoGenentechStony Brook UniversityEli Lilly and CompanyYork UniversityFORUM PharmaceuticalsBiogenNYU Langone Medical CenterState University of New York Upstate Medical UniversityEisaiSanofiClaret MedicalUniversity of RochesterSanofi GenzymeSyracuse UniversityTeva Pharmaceutical Industries
KeywordsMedicineMoodFeelingMultiple sclerosisExpanded Disability Status ScaleOdds ratioLogistic regressionLonelinessNursing homesPessimismCross-sectional studyPhysical therapyPsychiatryNursingInternal medicinePsychology

Abstract

fetched live from OpenAlex

Background: Multiple sclerosis (MS) is a chronic, progressively disabling condition of the central nervous system. We sought to evaluate and compare mood states in patients with MS with increased disability residing in nursing homes and those receiving home-based care. Methods: We conducted a cross-sectional analysis of the New York State Multiple Sclerosis Consortium to identify patients with MS using a Kurtzke Expanded Disability Status Scale (EDSS) score of 7.0 or greater. The nursing home group was compared with home-based care patients regarding self-reported levels of loneliness, pessimism, tension, panic, irritation, morbid thoughts, feelings of guilt, and fatigue using independent-samples t tests and χ2 tests. Multivariate logistic regression analyses were used to investigate risk-adjusted differences in mood states. Results: Ninety-four of 924 patients with EDSS scores of at least 7.0 lived in a nursing home (10.2%). Nursing home patients were less likely to use disease-modifying therapy and had higher mean EDSS scores compared with home-based patients. However, nursing home patients were less likely than home-based patients to report fatigue (odds ratio [OR] for no fatigue, 3.8; 95% CI, 2.1–7.2), feeling tense (OR for no tension, 1.7; 95% CI, 1.1–2.7), and having feelings of pessimism (OR for no pessimism, 1.8; 95% CI, 1.2–2.8). Conclusions: The nursing home patients with MS were less likely to report fatigue, pessimism, and tension than those receiving home-based care. Further studies should examine ways of facilitating a greater degree of autonomy and decision-making control in MS patients receiving home-based care.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.030
Threshold uncertainty score0.347

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.038
GPT teacher head0.325
Teacher spread0.287 · 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

Citations6
Published2017
Admission routes1
Has abstractyes

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