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Record W4312203513 · doi:10.1177/20494637221147185

Reliability and minimal detectable difference of pressure pain thresholds in a pain-free population

2022· article· en· W4312203513 on OpenAlexaff
Ryan G. L. Koh, Tracy M. Paul, Karlo Nesovic, Daniel W. D. West, Dinesh Kumbhare, Richard D. Wilson

Bibliographic record

VenueBritish Journal of Pain · 2022
Typearticle
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsUniversity of TorontoToronto Rehabilitation InstituteUniversity Health Network
Fundersnot available
KeywordsIntraclass correlationMedicineReliability (semiconductor)Inter-rater reliabilityPhysical therapyPopulationSignificant differenceInternal medicinePsychometricsStatisticsClinical psychologyRating scaleMathematics

Abstract

fetched live from OpenAlex

The objective of this work was to evaluate the inter-rater and intra-rater reliability and minimal detectable difference (MDD) of pressure pain thresholds (PPTs) in pain-free participants with two examiners over two consecutive days in a cross-sectional study design. Examiners used a standardized method to measure and locate a specific testing site over tibialis anterior for PPT testing with a hand-held algometer. The mean of each examiner’s three PPT measurements was used to calculate the intraclass correlation coefficient, inter-rater reliability, and intra-rater reliability. The minimal detectable difference (MDD) was calculated. Eighteen participants were recruited (11 female). The inter-rater reliability was 0.94 and 0.96 on day 1 and day 2, respectively. Intra-rater reliability for the examiners was 0.96 and 0.92 on day 1 and day 2, respectively. The MDD on day 1 was 1.24 kg/cm 2 (CI: 0.76–2.03) and the MDD on day 2 was 0.88 kg/cm 2 (CI: 0.54–1.43). This study demonstrates high inter- and intra-rater reliability and the MDD values for this method of pressure algometry.

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)0.010
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.139
Threshold uncertainty score0.399

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.003
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 head0.011
GPT teacher head0.234
Teacher spread0.223 · 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

Citations13
Published2022
Admission routes1
Has abstractyes

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