Zależności pomiędzy czynnikami psychologicznymi a wybranymi zmiennymi wśród pacjentów z przewlekłym, szyjnym zespołem zaburzeń strukturalnych
Bibliographic record
Abstract
Background. The aim of the study was to explore the relationships among pain, motion of the cervical spine, duration of the current episode or number of previous episodes and psychological factors in patients with chronic cervical derangement syndrome. Material and methods. 63 patients with cervical radiculopathy who achieved centralization of symptoms during an examination participated in the study. Intensity of pain was rated on a visual analog scale (VAS). The McGill Pain Questionnaire was used to assess various aspects of pain, and CROM goniometer was used to analyze the motion of the cervical spine. Anxiety was measured using Spielberger State-Trait Anxiety Inventory (STAI) while emotions were measured using Adjective Scale for Measuring Emotional State (SE-T Scale). Pearson’s r correlation was used in the statistical analysis. Results: Patients with higher level of trait and state anxiety are characterized by a higher intensity of overall pain and pain localized proximally to the cervical spine. Trait anxiety was positively correlated with all indexes of the McGill Pain Questionnaire and duration of the current episode. Negative emotions revealed positive relationships with the number of previous episodes or the intensity of cervical pain, and positive emotions correlated with the level of pain in the upper extremities. Trait and state anxiety negatively correlated with protraction. Conclusions. Multidisciplinary treatment ought to include activities aimed at reducing negative emotions of the patient through relaxation trainings or psycho-educational programs. Analysis of treatment efficacy should also include the emotional state of patients. Patients should be educated about the role of topography of symptoms in the diagnosis of cervical radiculopathy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".