Development of the Sensory–Motor Dysfunction Questionnaire and Pilot Reliability Testing
Bibliographic record
Abstract
Both chronic and recurrent spinal pain alter sensorimotor integration (SMI), which is demonstrated using complex neurophysiological techniques. Currently, there is no patient-reported outcome measure that documents and/or assesses SMI in populations with spinal problems. The purpose of this study was to develop the Sensory–Motor Dysfunction Questionnaire (SMD-Q) and assess its test–retest reliability and internal consistency in individuals with recurrent spinal pain. The SMD-Q was developed based on the existing literature on motor control disturbances associated with disordered SMI. The initial SMD-Q drafts underwent review by two separate panels of subject matter experts and a focus group with subclinical spine pain. Their suggestions were incorporated into the questionnaire prior to reliability testing. The questionnaire was administered twice at a seven-day interval using QualtricsTM. A total of 20 participants (14 females and 6 males; 20.95 ± 2.46 years of age) completed the study. Quadratic weighted kappa (Kw) was used to assess test–retest reliability and Cronbach’s alpha (α) was used to assess internal consistency. Four items had a Kw < 0.40, seven had a 0.40 < Kw < 0.75, and one had a Kw > 0.75 (excellent agreement), with excellent internal consistency (α > 0.90). The pilot SMD-Q appears to reliably measure altered SMI, suggesting that revisions and testing with a larger sample are worth pursuing.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".