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Record W2010696959 · doi:10.3109/09638288.2011.626835

A systematic review of psychometric evaluations of outcome assessments for complex regional pain syndrome

2011· review· en· W2010696959 on OpenAlexaff
Tara Packham, Joy C. MacDermid, James L. Henry, James R. Bain

Bibliographic record

VenueDisability and Rehabilitation · 2011
Typereview
Languageen
FieldMedicine
TopicPain Management and Treatment
Canadian institutionsMcMaster University
Fundersnot available
KeywordsComplex regional pain syndromePsychometricsRehabilitationClinical psychologyReliability (semiconductor)Construct validityPhysical therapyMEDLINEPsychologyPsychometric testingPopulationPatient-reported outcomeSystematic reviewQuality of life (healthcare)MedicinePhysical medicine and rehabilitationPsychotherapistCronbach's alpha

Abstract

fetched live from OpenAlex

PURPOSE: To conduct a systematic review of the quality and extent of psychometric examinations of disease-specific outcome measures for complex regional pain syndrome (CRPS). METHODS: Health database searches yielded 23 papers covering 19 assessment instruments. Each article was scored for quality using a 12-item structured tool; data were also extracted for comparison of tool content. RESULTS: Article quality ratings ranged from 25 to 88%. Six of the tools were specific to the upper extremity; 5 for the lower extremities while the remaining 8 were general. Many 'general' tools focused on a single construct, such as pain, skin temperature or allodynia. Most psychometric data was based on small studies (mean n=33); only one study addressed all relevant issues of reliability, validity and responsiveness. CONCLUSIONS: Despite the variety of outcome measurement tools reported for CRPS rehabilitation, large gaps in both comprehensiveness and supporting psychometric evidence remain. Comprehensive, relevant and psychometrically sound tools for monitoring treatment outcomes are needed to address the pain and functional limitations experienced by this population.

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)0.006
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.057
Threshold uncertainty score0.788

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
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 head0.208
GPT teacher head0.476
Teacher spread0.268 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations24
Published2011
Admission routes1
Has abstractyes

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