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Physical Symptoms and Signs and Chronic Pain

2001· review· en· W2331524101 on OpenAlexaff
Judith Hunter

Bibliographic record

VenueClinical Journal of Pain · 2001
Typereview
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGeneralizability theoryMedicineChronic painObservational studyPhysical therapyPain catastrophizingLow back painNeck painPhysical medicine and rehabilitationPhysical disabilityDistressClinical psychologyAlternative medicineInternal medicinePsychology

Abstract

fetched live from OpenAlex

OBJECTIVE: Do physical findings that are used to indicate location and extent of tissue damage and a measure of the severity of initial pain predict subsequent reports of pain and of disability? METHODOLOGY: A standardized literature search identified one systematic review and 12 observational studies (9 low back pain, 2 neck pain, and 1 carpal tunnel syndrome) to provide evidence about these questions. RESULTS: Most studies were of specific populations. These studies were useful studies of predictors, but they have limited generalizability. Exclusions and loss of subjects at follow-up in some studies also limited generalizability. Conclusions were made cautiously, because some factors with statistical correlations with chronic pain were not plausible predictors. CONCLUSIONS: The studies provide moderate evidence (level 2) that reports of the intensity of pain in acute musculoskeletal injury predict subsequent reports of pain. There is limited evidence (level 3) that the location and extent of injury predict reports of pain and poor functional activity outcomes. There is moderate evidence (level 2) that physical symptoms and signs cannot be considered individual predictors of chronic pain disability as measured by participation outcomes. Instead, in the transition from subacute to chronic pain disability, functional disability and psychological distress play a more important role than pain intensity.

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)0.012
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.979
Threshold uncertainty score0.735

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.054
GPT teacher head0.430
Teacher spread0.376 · 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 designOther design
Domainnot available
GenreReview

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

Citations25
Published2001
Admission routes1
Has abstractyes

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