Evaluation of effectiveness Postisometric Muscle Relaxationand Classical Massage in the Treatment of Neck Pain Syndromes
Bibliographic record
Abstract
Introduction: Ailments located in the cervical spine are a serious clinical problem. The aim of the study was to evaluate the effectiveness of Postisometric Relaxation (PIR) and Classical Massage (CM) in analgesic therapy in patients with neck chronic increased muscle tension. Material and methods: The study was carried out in a group of 36 adults (mean age 49.5±8.59 years, 29 women) with neck increased muscle tension due to overload changes. Patients were randomly assigned into two groups. The therapy consisted of 10 PIR or CM procedures. VAS (Visual Analogue Scale), NDI (Neck Disability Index), SF-MPQ (McGill Pain Questionnaire-Short Form by Melzack) were used. The trigger points were assessed and the mobility of the cervical spine was measured. Results: A statistically significant reduction in pain was obtained in both groups (improvement in the PIR group: 70%±29, CM 55%±27). In the PIR group, trigger points were completely eliminated in 50% of subjects and in the CM group in 38.9%. There was a statistically significant reduction in the NDI in both groups (improvement in the PIR group 70%±26, CM 48%±29). In both groups, a statistically significant increase in the mobility of the cervical spine in all directions was observed (extension, lateral flexion and rotation were statistically significantly better in the PIR group). Conclusions: PIR and CM statistically significantly reduce pain, improve health, effectively eliminate trigger points and increase the mobility of the cervical spine. PIR better increases the range of extension, lateral flexion and rotation of the cervical spine.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".