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Record W2052653514 · doi:10.2519/jospt.2014.4691

The Current Perception Threshold Test Differentiates Categories of Mechanical Neck Disorder

2014· article· en· W2052653514 on OpenAlexaff
Zakir Uddin, Joy C. MacDermid, Victoria Galea, Anita Gross, M.R. Pierrynowski

Bibliographic record

VenueJournal of Orthopaedic and Sports Physical Therapy · 2014
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineTest (biology)PerceptionPhysical medicine and rehabilitationNeck painAudiologyCurrent (fluid)Physical therapyNeuroscienceAlternative medicinePathology

Abstract

fetched live from OpenAlex

STUDY DESIGN: Cross-sectional discriminative analysis. OBJECTIVE: To determine whether current perception threshold (CPT) can differentiate between categories of patients with mechanical neck disorders (MNDs). BACKGROUND: Neck pain is the third most common musculoskeletal disorder and affects a third of all adults each year. It can present as neck pain without musculoskeletal signs, neck pain with musculoskeletal signs but no neurological signs, or neck pain with neurological signs. CPT testing can assess altered sensory perception that may reflect neurological changes. METHODS: Patients with MNDs (n = 106) were classified into 3 groups, based on a standardized musculoskeletal examination process performed by an experienced physiotherapist who was blinded to CPT scores. The 3 groups were defined as neck pain without musculoskeletal signs (MND I) (n = 60), neck pain with musculoskeletal signs (MND II) (n = 29), and neck pain with neurological signs (MND III) (n = 17). A rapid protocol of CPT testing was performed at 3 frequencies (5, 250, and 2000 Hz), using 3 dermatomal locations on the hand. A 1-way analysis of variance with post hoc comparison and effect sizes was calculated to compare the mean CPT scores between the groups. A binary logistic-regression model was used to predict probability of higher CPT in MND III and to create a receiver-operating-characteristic curve. RESULTS: Mean CPT differed significantly across the 3 MND groups (MND I, 9.7; MND II, 10.6; and MND III, 11.8; P<.001; η(2) = 0.6). Post hoc comparisons indicated differences between MND I and MND II (P = .05) and between MND II and MND III (P = .01) that had large effect sizes (MND I versus II, d = 1 and MND II versus III, d = 2.2). CPT testing was able to distinguish between MND II and III when a threshold value of greater than 11 was used to indicate MND III. The predicted probability of abnormal CPT in MND III had an estimated 73% sensitivity and 81% specificity; the odds ratio was 11.5 (P = .001) for the differentiation capacity of CPT between MND II and III, with a cutoff of 11. The area under the receiver-operating-characteristic curve was 0.84 (95% confidence interval: 0.72, 0.96; P<.001). CONCLUSION: CPT testing has moderate discriminatory accuracy, specificity, and sensitivity for classification of MND categories into neck pain with or without neurological signs. J Orthop Sports Phys Ther 2014;44(7):532-540. Epub 10 May 2014. doi:10.2519/jospt.2014.4691.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.008
GPT teacher head0.270
Teacher spread0.262 · 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 source (direct Gemma or distilled Codex), 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

Citations21
Published2014
Admission routes1
Has abstractyes

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