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Record W2148147315 · doi:10.3138/physio.60.3.239

Pain Profiles and Psychosocial Distress Symptoms in Workers with Low Back Pain

2008· article· en· W2148147315 on OpenAlexaffvenue
Nomusa Mngoma, Marc Corbière, Joan M. Stevenson

Bibliographic record

VenuePhysiotherapy Canada · 2008
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsQueen's UniversityUniversité de SherbrookeProvidence Health Care
Fundersnot available
KeywordsPsychosocialMedicineAnxietyPhysical therapyDepression (economics)RehabilitationDistressLow back painPsychological interventionPain catastrophizingBack painCluster (spacecraft)Chronic painPsychiatryClinical psychologyAlternative medicine

Abstract

fetched live from OpenAlex

PURPOSE: The current study investigated the pain profiles of patients with subacute non-specific low back pain attending an outpatient return-to-work rehabilitation programme. Differences in symptoms of distress (depression and anxiety) and return to work between the pain-profile groups were assessed. METHODS: Sixty-five volunteers who met the eligibility criteria and had complete follow-up data were included in the analysis. The mean age was 38.8 years (minimum 18, maximum 64); 38 (58.5%) were men. The median time since onset of low back pain was 30 days. Cluster analysis was used to categorize patients into groups according to pain severity scores (VAS). RESULTS: Two distinct clusters-severe pain and moderate pain-emerged. There were significant differences in depressive and anxiety symptoms between the pain profiles. Further, return-to-work rates varied significantly between the two groups (31% in the severe pain cluster compared to 90% in the moderate pain cluster). CONCLUSION: Although both groups showed significant improvements in depression and anxiety symptoms over time, the severe pain cluster scored higher at discharge (higher scores indicating worse outcomes). These results highlight the importance of early identification of sub-groups at risk so that rehabilitation interventions can be focused with the goal of minimizing long-term 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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score0.811

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.004
GPT teacher head0.238
Teacher spread0.234 · 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

Citations9
Published2008
Admission routes2
Has abstractyes

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