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Record W1514773312 · doi:10.1111/pme.12588

Assessment of Pain Intensity in Clinical Trials: Individual Ratings vs Composite Scores

2014· article· en· W1514773312 on OpenAlexaff
Mark P. Jensen, Catarina Tomé‐Pires, Ester Solé, Mélanie Racine, Elena Castarlenas, Rocío de la Vega, Jordi Miró

Bibliographic record

VenuePain Medicine · 2014
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsWestern UniversityLawson Health Research Institute
Fundersnot available
KeywordsGeneralizability theoryMedicinePhysical therapyClinical trialReliability (semiconductor)Physical medicine and rehabilitationPsychologyInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: To evaluate the reliability of findings suggesting that composite scores made up of just two ratings of recalled pain may be adequately reliable and valid for assessing outcome in pain clinical trials. DESIGN: Secondary analyses of data from a study where the responsivity of the outcome measures was a critical concern; that is, a study with few subjects testing the effects of a treatment that had only modest effects. Ten adults with spinal cord injury rated four domains of pain intensity (current pain and 24-hour recalled worst, least, and average pain) on four occasions before and after 12 sessions of neurofeedback treatment. We evaluated the reliability and validity of four single ratings and 16 different composite scores. RESULTS: None of the single-item scales performed adequately. However, composite scores made up of two items or more yielded consistent effect size estimates. CONCLUSIONS: The findings provide additional evidence that two-item composite scores may be adequate for assessing the primary outcome of pain intensity in chronic pain clinical trials. Additional research is needed to further establish the generalizability of these findings.

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)0.145
metaresearch head score (Gemma)0.058
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.087
Threshold uncertainty score0.950

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1450.058
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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 head0.070
GPT teacher head0.433
Teacher spread0.363 · 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; both teacher heads agree on what is shown here.

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

Citations34
Published2014
Admission routes1
Has abstractyes

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