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Assessing Persistent Pain and Its Relation to Affective Distress, Depressive Symptoms, and Pain Catastrophizing in Patients with Chronic Wounds

2004· article· en· W2058187469 on OpenAlexaboutno aff
Randy S. Roth, Julie C. Lowery, Jennifer B. Hamill

Bibliographic record

VenueAmerican Journal of Physical Medicine & Rehabilitation · 2004
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsnot available
Fundersnot available
KeywordsPain catastrophizingMedicineMcGill Pain QuestionnairePhysical therapyChronic painDistressDepression (economics)Coping (psychology)Visual analogue scalePsychiatryClinical psychology

Abstract

fetched live from OpenAlex

OBJECTIVE: The purpose of this study was to examine pain experience among patients with chronic wounds, assess the utility of pain assessment scales for chronic wound-related pain, and determine the relation of wound-related pain to wound stage, affective distress, depressive symptoms, and pain catastrophizing. DESIGN: In this cross-sectional study of patients with a mix of chronic wounds (n = 69) recruited for a study evaluating a telemedicine system for assessing chronic wounds, 19 men (12 with spinal cord injury) with wound-related pain were identified. Questionnaires included the Numerical Pain Rating Scale, McGill Pain Questionnaire, Brief Symptom Inventory, Center for Epidemiologic Studies Depression Scale, and the catastrophizing scale of the Coping Strategies Questionnaire. RESULTS: The McGill Pain Questionnaire was more sensitive to pain experience than a single rating of pain intensity. Wound stage was positively related to severity of pain. Pain catastrophizing was positively related to pain intensity and higher levels of affective distress and depressive symptoms. CONCLUSIONS: Pain associated with chronic wounds is a significant clinical challenge for both patients and health practitioners.

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)0.003
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 score0.663
Threshold uncertainty score0.500

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.260
Teacher spread0.257 · 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
Published2004
Admission routes1
Has abstractyes

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