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Record W2754336566

Reported pain in multiple sclerosis (MS) and its relationship with affect and attention

2007· dissertation· en· W2754336566 on OpenAlexaboutno aff
Kathryn Elizabeth Hoffman

Bibliographic record

VenueOpenGrey (Institut de l'Information Scientifique et Technique) · 2007
Typedissertation
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsDistressMoodPsychologyCognitionPopulationClinical psychologyCoping (psychology)Affect (linguistics)RecallPsychiatryMedicineCognitive psychology
DOInot available

Abstract

fetched live from OpenAlex

Pain is an important part of MS symptomatology. Studies, with other populations, suggest distress is associated with pain. However, models of the influence of psychological factors on have not been carefully applied and tested with the MS population. The hypothesis was: many patients do not classify much of their sensory disturbance as due to their conceptual framework and this may affect the relationship between and distress. A model of these factors was developed for MS patients. A clinic sample of MS patients, expected to have varying degrees of subjective pain, was recruited. Standard, adapted and new measures were used to characterise the population along the following dimensions: pain, level of cognitive ability (general intelligence and working memory) and cognitive bias, mood, and coping styles. Amount of distress was assessed using a semantic differential measure of wellbeing/distress, Survey of Pain Attitudes and Coping with MS Scale. A Pain Discomfort Scale was adapted to discern differences between people reporting pain versus those reporting discomfort. Pain cognitive-processing bias was explored using assessments including a stem completion task, an experimental recall task using and illness words and a restructured Hayling sentence completion task. Power calculations showed that with 100 patients a detectable correlation would be 0.28 (p=0.05, power = 80%). Measures were compared using paired t-tests for repeated measures, independent t-tests for measures across patients, and regression modelling. McGill adjectives chosen were similar across both high and low responders. Participants reporting pain experienced significantly greater physical impact of MS whereas participants reporting discomfort experienced greater emotional distress. Cognitive bias towards pain, illness and MS related material was not linked with overall or disease state but with coping styles. A model of how emotional stressors affect reported in MS was created.

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)0.006
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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.074
GPT teacher head0.338
Teacher spread0.265 · 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

Citations0
Published2007
Admission routes1
Has abstractyes

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