Relationship Between Functional Evaluation Measures and Self-Assessment in Nonacute Low Back Pain
Bibliographic record
Abstract
STUDY DESIGN: The correlations between objective biomechanical indicators of function and self-assessment scores were examined retrospectively for 91 subjects with nonacute low back pain. OBJECTIVES: To examine the correlation between self-assessment, trunk range of motion (ROM), velocity, and complex mechanical coordination patterns of the spine in nonacute low back pain. SUMMARY OF BACKGROUND DATA: In low back pain, there is often little concordance between pain, physical impairment, and disability. Use of range of motion and velocity to enhance objectivity in impairment evaluations has been ineffectual. In this study, two hypotheses were examined: range of motion and velocity are controllable and inherently correlated with self-assessment; complex spinal coordination patterns such as range of lordosis cannot be controlled and are independent of self-assessment. METHODS: Self-assessment questionnaires were administered, and indexes of spinal motion and coordination were measured through skin marker kinematics. The correlation between self-assessments and biomechanical measures was determined. RESULTS: Self-assessments of function were significantly correlated with parameters prone to regulation: range of motion, velocity, and load lifted. In contrast, little correlation was found with measures of complex spinal coordination less susceptible to conscious or affective regulation, namely, range of lordosis and estimated segmental mobility. This effect was magnified with increased load. Self-assessment scores were significantly poorer among insurance referrals, regardless of functional status. CONCLUSIONS: Simple parameters of the functional examination, such as range of motion and velocity, are strongly correlated with cognitive state, and thus the information they supply is less than ideal. Complex spinal coordination is a better indicator of the degree of spinal dysfunction and enhances the process of differentiating between pain, disability, and functional impairment.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".