Effectiveness of proprioceptive neuromuscular facilitative stretching combined with administration of Diclofenac compared to proprioceptive neuromuscular facilitative stretching and placebo medication for the treatment of cervical facet syndrome
Bibliographic record
Abstract
The purpose of this study was to test the Effectiveness of Proprioceptive Neuromuscular Facilitative Stretching combined with administration of Diclofenac compared to Proprioceptive Neuromuscular Facilitative Stretching and placebo medication for the treatment of Cervical Facet Syndrome in a clinical experimental setting. Neck pain is a common disorder, which can often be attributed to mechanical dysfunction of the cervical spine. The patient with facet syndrome may complain of sudden onset of unilateral neck pain, often with referred pain. Muscle spasm is usually present causing restricted movement. Pain increases with movement and is relieved by rest. The pain is aggravated by hyperextension and relieved by flexion and often follows a sclerotomal rather than a dermatomal pattern. Forty subjects with mechanical neck pain were screened for facet syndrome and randomly divided into two groups of twenty. Each patient received Proprioceptive Neuromuscular Facilitative (PNF) stretching of the Posterior Cervical and Trapezius musculature. In conjunction with this, half the patients received Cataflam D while the other half received placebo medication. The patients were treated five times over a period of two weeks. Both groups were evaluated in terms of subjective and objective clinical findings by making use of questionnaires (Numerical Pain Rating Scale 101, Short Form McGill Pain Questionnaire and the CMCC) and algometer and goniometer measurements respectively. The data was collected at the initial, middle and final treatments for each patient.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".