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Record W112287749 · doi:10.2340/1650197790227377

Chronic low-back pain: intercorrelation of repeated measures for pain and disability

2020· article· en· W112287749 on OpenAlexaboutno aff
Mats Grönblad, Asko Lukinmaa, YT Konttinen

Bibliographic record

VenueJournal of Rehabilitation Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsnot available
Fundersnot available
KeywordsMcGill Pain QuestionnairePhysical therapyVisual analogue scaleMedicineRepeated measures designPain scalePain assessmentRank correlationPain catastrophizingChronic painPhysical medicine and rehabilitationPain management

Abstract

fetched live from OpenAlex

Subjective experience of pain and disability was assessed for 4-5 weeks on a weekly basis in 14 consecutive out-patients complaining of low-back pain and/or leg pain that had lasted for at least 6 months. The following measures were used for assessment: a visual analogue scale (VAS) (present pain and worst pain during preceding 2 weeks), a short-form McGill Pain Questionnaire (SF-MPQ), the Pain Disability Index (PDI) and the pain drawing. In addition, psychological variables of pain experience were evaluated with the Comprehensible Psychopathological Rating Scale (CPRS). When the median of the variation coefficient for repeated measures was compared, the most stable measures were, in rank order: PDI, total number of words chosen in the SF-MPQ and worst pain during the preceding two weeks (VAS). The Spearman correlation showed statistically significant intercorrelation for present pain assessed with the VAS score, for the sensory word score of the SF-MPQ and for the PDI. Especially the PDI, which represents a global score for disability, showed very little test-retest variability and a high intercorrelation with the other methods of assessment, i.e. the pain drawing, the VAS scale for pain and the SF-MPQ.(ABSTRACT TRUNCATED AT 250 WORDS)

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)0.008
metaresearch head score (Gemma)0.040
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.388
Threshold uncertainty score0.968

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.040
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.017
GPT teacher head0.292
Teacher spread0.275 · 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.

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

Citations43
Published2020
Admission routes1
Has abstractyes

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