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Record W2300174607 · doi:10.1155/2010/617129

The Trajectory of Chronic Pain: Can a Community‐Based Exercise/Education Program Soften the Ride?

2010· article· en· W2300174607 on OpenAlexafffund
Ruth Dubin, Cheryl E. King‐VanVlack

Bibliographic record

VenuePain Research and Management · 2010
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsKingston Health Sciences CentreQueen's University
FundersValeant Pharmaceuticals InternationalPurdue UniversitySanofi
KeywordsPhysical medicine and rehabilitationPhysical therapyTrajectoryChronic painPsychologyMedicinePhysics

Abstract

fetched live from OpenAlex

The entire primary care record of six patients attending a community-based education/exercise self-management program for chronic noncancer pain (YMCA Pain Exercise/Education Program [Y-PEP]) was reviewed. Medical visits, consultations and hospital admissions were coded as related or unrelated to their pain diagnoses. Mood disruption, financial concerns, conflicts with employers/insurers, analgesic doses, medication side effects and major life events were also recorded. The 'chronic pain trajectory' resembled a roller coaster with increased health care visits at the time of initial injuries and during 'crises' (reinjury, conflict with insurers/employers, failed back-to-work attempts and life events). Visits decreased when conflicts were resolved. Analgesic doses increased during 'crises' but did not fall after resolution. After attending Y-PEP, health care use fell for four of six patients and two returned to work. Primary care physicians need to recognize the functional limitations and psychosocial complications experienced by their chronic pain patients. A program such as Y-PEP may promote active self-management strategies resulting in lowered health care use.

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)0.017
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.975
Threshold uncertainty score0.582

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.033
GPT teacher head0.367
Teacher spread0.335 · 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
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

Citations14
Published2010
Admission routes2
Has abstractyes

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