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Record W1979990341 · doi:10.1016/j.pmrj.2009.03.003

Fatigue in Post‐poliomyelitis Syndrome: Association With Disease‐Related, Behavioral, and Psychosocial Factors

2009· article· en· W1979990341 on OpenAlexafffund
Daria A. Trojan, Douglas L. Arnold, Stan Shapiro, Amit Bar‐Or, Ann Robinson, Jean‐Pierre Le Cruguel, Sridar Narayanan, Maria Carmela Tartaglia, Zografos Caramanos, Deborah Da Costa

Bibliographic record

VenuePM&R · 2009
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Immunology Research
Canadian institutionsWestern UniversityMcGill UniversityMcGill University Health CentreMontreal Neurological Institute and Hospital
FundersMultiple Sclerosis Society of Canada
KeywordsMedicinePsychosocialPhysical therapyDepression (economics)FibromyalgiaBiopsychosocial modelPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine the biopsychosocial correlates of general, physical, and mental fatigue in patients with postpoliomyelitis syndrome (PPS) by assessing the additional contribution of potentially modifiable factors after accounting for important nonmodifiable disease-related factors. It was hypothesized that disease-related, behavioral, and psychosocial factors would contribute in different ways to general, physical, and mental fatigue in PPS and that a portion of fatigue would be determined by potentially modifiable factors. DESIGN: Cross-sectional study. SETTING: A tertiary university-affiliated hospital post-polio clinic. PATIENTS: Fifty-two ambulatory patients with PPS who were not severely depressed were included. ASSESSMENT OF RISK FACTORS: Potential correlates for fatigue included disease-related factors (acute polio weakness, time since acute polio, PPS duration, muscle strength, pain, forced vital capacity, maximum inspiratory pressure, maximum expiratory pressure, body mass index, disability, fibromyalgia), behavioral factors (physical activity, sleep quality), and psychosocial factors (depression, stress, self-efficacy). MAIN OUTCOME MEASUREMENTS: Fatigue was assessed with the Multidimensional Fatigue Inventory (MFI; assesses fatigue on 5 subscales) and the Fatigue Severity Scale (FSS). RESULTS: Multivariate models were computed for MFI General, Physical, and Mental Fatigue. Age-adjusted multivariate models with nonmodifiable factors included the following predictors of (1) MFI General Fatigue: maximum inspiratory pressure, fibromyalgia, muscle strength; (2) MFI Physical Fatigue: maximum expiratory pressure, muscle strength, age, time since acute polio; and (3) MFI Mental Fatigue: none. The following potentially modifiable predictors made an additional contribution to the models: (1) MFI General Fatigue: stress, depression; (2) MFI Physical Fatigue: physical activity, pain; and (3) MFI Mental Fatigue: stress. CONCLUSIONS: PPS fatigue is multidimensional. Different types of fatigue are determined by different variables. Potentially modifiable factors account for a portion of fatigue in PPS.

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 score0.005
Threshold uncertainty score0.293

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 head0.024
GPT teacher head0.336
Teacher spread0.312 · 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

Citations40
Published2009
Admission routes2
Has abstractyes

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