A Higher-Order Analysis Supports Use of the 11-Item Version of the Tampa Scale for Kinesiophobia in People With Neck Pain
Bibliographic record
Abstract
BACKGROUND: Despite increasing clinical and research use of the 11-item version of the Tampa Scale for Kinesiophobia (TSK-11) in people with neck pain, little is known about its measurement properties in this population. OBJECTIVE: The purpose of this study was to rigorously evaluate the measurement properties of the TSK-11 when used in people with mechanical neck pain. DESIGN: This study was a secondary analysis of 2 independent databases (N=235) of people with mechanical neck pain of primarily traumatic origin. METHODS: The TSK-11 was subjected to Rasch analysis and subsequent evaluation of concurrent associations with the Neck Disability Index and a numeric rating scale for pain intensity. RESULTS: The TSK-11 conformed well to the Rasch model for interval-level measurement, but less so for acute or nontraumatic etiologies. A transformation matrix suggested that small changes at the extremes of the scale are more meaningful than in the middle. Cross-sectional convergent validity testing suggested relationships of expected magnitude and direction compared with pain intensity and neck-related disability. The use of the linearly transformed TSK-11 led to potentially important differences in distribution of data compared with use of the raw scores. LIMITATIONS: The sample size was slightly smaller than desired for Rasch analysis. The 2 databases were similar in terms of symptom duration, but differed in pain intensity and age. CONCLUSIONS: The TSK-11 can be considered an interval-level measure when used in people with neck pain. It provides potentially important information regarding the nature of neck-related disability. Clinically important difference may not be consistent across the range of the scale.
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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 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".