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Record W2007533243 · doi:10.1016/s0304-3959(02)00206-3

An experimental investigation of the relation between catastrophizing and activity intolerance

2002· article· en· W2007533243 on OpenAlexafffund
Michael J. Sullivan, Wendy M. Rodgers, Philip Wilson, Gordon J. Bell, Terra C. Murray, Shawn N. Fraser

Bibliographic record

VenuePain · 2002
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversity of AlbertaDalhousie University
FundersSocial Sciences and Humanities Research Council of CanadaCanadian Institutes of Health Research
KeywordsPain catastrophizingMoodPhysical therapyDelayed onset muscle sorenessPsychologyMedicinePhysical medicine and rehabilitationClinical psychologyChronic painInternal medicineMuscle damage

Abstract

fetched live from OpenAlex

The present study examined the value of a measure of catastrophizing as a predictor of activity intolerance in response to delayed onset muscle soreness (DOMS). A sample of 50 (17 men, 33 women) sedentary undergraduates participated in an exercise protocol designed to induce muscle soreness and were asked to return 2 days later to perform the same physical maneuvers. Participants performed five strength exercises that emphasized the eccentric component of the muscle contraction in order to induce DOMS. Dependent variables of interest were the proportion reduction in total weight lifted, and the number of repetitions. Analyses revealed that catastrophizing, assessed prior to the first exercise bout, was significantly correlated with negative mood, pain and with reduction in weight lifted. Regression analyses revealed that catastrophizing predicted reductions in weight lifted even after controlling for pain and negative mood. These findings extend previous research in demonstrating that catastrophizing is associated with objective indices of activity intolerance associated with pain. Implications of these findings for understanding pain-related disability are addressed.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.526
Threshold uncertainty score0.115

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.023
GPT teacher head0.273
Teacher spread0.250 · 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

Citations128
Published2002
Admission routes2
Has abstractyes

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