Known-Group Validity and Sensitivity to Change in the Sensory-Motor Dysfunction Questionnaire in Individuals with Neck Pain: A Pilot Study
Bibliographic record
Abstract
Background/Objectives: Sensorimotor dysfunction is often reported in individuals experiencing neck pain, irrespective of severity and chronicity. The treatment of neck dysfunction has been found to positively impact sensorimotor integration (SMI), thereby improving sensorimotor function. However, no patient-reported outcome measure has been validated for discrimination between healthy individuals and neck pain patients with disordered SMI, nor has there been documentation of positive change in SMI following neck pain treatment.The purpose of this study was to administer the Sensory-Motor Dysfunction Questionnaire (SMD-Q), for the purposes of: (1) known-group validity between individuals without and with chronic neck pain (CNP) or subclinical neck pain (SCNP); (2) assess the questionnaire’s capacity to quantify changes in altered SMI following a tailored treatment plan. Methods: Part 1—Known Group Validity: The SMD-Q was administered to 30 neck pain (13 with CNP, and 17 with SCNP), and 30 healthy participants. Part 2—Sensitivity to Change: The SMD-Q was re-administered to neck pain participants following their tailored treatment plans (SCNP—8-weeks and CNP—12 weeks). Results: The SMD-Q can discriminate between healthy and neck pain participants (p ≤ 0.001), and may be sensitive to showing treatment effects (ηp2 = 0.162; large effect size (ES)), but the sample size was too small to determine if it can discriminate treatment effects between groups (ηp2 = 0.070; medium ES). Conclusions: Differing degrees of disordered SMI can be discriminated by the SMD-Q, but further research is needed to determine its sensitivity to treatment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".