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Record W2091850076 · doi:10.1016/j.pain.2010.08.005

Activity-related summation of pain and functional disability in patients with whiplash injuries

2010· article· en· W2091850076 on OpenAlexafffund
Michael Sullivan, Christian Larivière, Maureen J. Simmonds

Bibliographic record

VenuePain · 2010
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsInstitut de recherche Robert-Sauvé en santé et en sécurité du travailMcGill University
FundersCanadian Institutes of Health Research
KeywordsWhiplashPain catastrophizingPhysical therapyPsychologyPain tolerancePhysical medicine and rehabilitationDepression (economics)Chronic painMedicinePoison controlThreshold of painAnesthesia

Abstract

fetched live from OpenAlex

This study investigated the relation between repetition-induced summation of activity-related pain (RISP) and indicators of functional disability in a sample of 62 individuals who had sustained whiplash injuries. Participants completed measures of pain severity, pain catastrophizing, fear of movement and depression prior to lifting a series of 36 weighted canisters. An index of RISP was computed as the increase in pain reported by participants over successive lifts of the weighted canisters. Measures of functional disability included physical lifting tolerance, self-reported disability and perceived work demands. Regression analyses revealed that the index of RISP accounted for significant variance in measures of lifting tolerance and perceived work demands, even when controlling for age, sex and pain severity. The index of RISP was also significantly correlated with pain catastrophizing and pain duration. The discussion addresses the mechanisms by which physiological and psychological factors might contribute to increases in pain during repeated physical activity. Discussion also addresses whether RISP might represent a risk factor for problematic recovery outcomes following whiplash injury.

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)0.003
metaresearch head score (Gemma)0.002
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 score0.041
Threshold uncertainty score0.220

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
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 head0.004
GPT teacher head0.220
Teacher spread0.216 · 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

Citations67
Published2010
Admission routes2
Has abstractyes

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