Correlation between subjective patient assessment, therapist examination and objective assessment of postural stabilization in patients with low back pain
Bibliographic record
Abstract
The goal of this master's thesis was to determine if there is a correlation between subjective, objective, and clinical examinations of patients with low back pain (LBP). For subjective assessment, the Oswestry Disability Index and the Short-Form McGill Pain Questionnaire 2 (SF-MPQ-2) were used. Clinical examinations were performed by a physical therapist certified in the Dynamic Neuromuscular Stabilization (DNS) method, using the DNS examination protocol. Additionally, goniometry measurements of hip joint range of motion and spine mobility evaluations were conducted using Schober, Stibor, and Thomayer mobility tests. For objective assessment, posturography was used to evaluate the patients' postural stability. The outcomes of these measurements were statistically analysed, and correlations were determined. The main hypothesis was that a positive correlation exists between the performed examinations. The study included the evaluation of 28 patients with LBP who had no other serious health issues. A moderate correlation, according to the Spearman correlation coefficient, was identified between the Schober test and postural sway in the anteroposterior direction, as well as between the Stibor test and continuous pain analyzed via SF-MPQ-2, at a 1% level of statistical significance.
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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 | 0.001 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".