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Record W2036219924 · doi:10.1016/j.pain.2012.02.001

Pain-related fear predicts reduced spinal motion following experimental back injury

2012· article· en· W2036219924 on OpenAlexaff
Zina Trost, Christopher France, Michael Sullivan, James S. Thomas

Bibliographic record

VenuePain · 2012
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsMcGill University
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institutes of Health
KeywordsMedicineLumbarPhysical medicine and rehabilitationLow back painBack painPhysical therapyTrunkSurgery

Abstract

fetched live from OpenAlex

The current study examined the prospective relationship between pain-related fear and altered motor behavior, as well as perceived interference, among 51 healthy participants following induction of delayed-onset muscle soreness (DOMS) to the trunk extensor muscles. Healthy participants without history of back pain completed standardized reaches to high and low targets at self-paced and rapid speeds before and after induction of acute low back pain using a DOMS paradigm. Pain-related fear was assessed prior to DOMS induction. Three-dimensional joint motions of the thoracic spine, lumbar spine, and hip were recorded using an electromagnetic tracking device. DOMS-induced differences between high- and low-fear participants were observed for lumbar spine flexion, but not for thoracic or hip flexion. Pain-related fear scores were not predictive of lumbar flexion during baseline, but predicted reduced lumbar flexion during self- and fast-paced trials to low target locations once DOMS was induced. Pain-related fear was likewise predictive of perceived interference in life activities following DOMS induction. The findings suggest that initially pain-free individuals with high pain-related fear adopt avoidant spinal strategies during common reaching movements shortly after injury is sustained, which may comprise a risk factor for future pain and disability.

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)0.005
metaresearch head score (Gemma)0.001
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 score0.827
Threshold uncertainty score0.513

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
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 head0.014
GPT teacher head0.298
Teacher spread0.284 · 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

Citations52
Published2012
Admission routes1
Has abstractyes

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