The Role of Selected Variables in the Diagnosis of Cervical Derangement Syndromes
Bibliographic record
Abstract
BACKGROUND: The study analyzed correlations between selected variables in cervical derangement syndromes. MATERIAL AND METHODS: We analyzed data from 63 patients regarding pain (VAS, McGill Pain Questionnaire), mobility (CROM goniometer), dizziness, nausea, the duration of the current episode, and the number of previous episodes (history). Student's t and chi(2) tests and Pearson's r correlation were used. RESULTS: Overall pain intensity correlated positively with the indexes of the McGill Pain Questionnaire, the duration of the current episode, intensity of the proximal and distal symptoms and negatively with protraction or extension. Headache correlated positively with neck pain and negatively with retraction. Neck pain cor-related negatively with multiple cervical movements and positively with intensity of the distal symptoms. A positive relationship between shoulder and upper limb pain was observed. Patients with higher overall pain intensity or lower shoulder pain intensity experienced dizziness more often. The duration of the current episode correlated positively with the number of previous episodes, the frequency of nausea, limited extension and limited protraction. Nausea coexisted with dizziness and reduced protraction. The degree of flexion restriction correlated positively with the number of previous episodes. CONCLUSIONS: 1. Overall and proximal pain intensity, mobility of the cervical spine, the duration of the current episode and dizziness are useful in diagnosis of cervical derangement syndromes. 2. Intensity of the distal symptoms, the number of previous episodes and nausea should be particularly monitored.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".