Cervical flexor muscle training reduces pain, anxiety, and depression levels in patients with chronic neck pain by a clinically important amount: A prospective cohort study
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Neck pain is the fourth leading cause of disability in the United States and exerts an important socio-economic burden around the world. The aims of this study were to determine the effectiveness of deep and superficial flexor muscle training in addition to home-based exercises in reducing chronic neck pain and anxiety/depression levels. METHODS: This was a prospective cohort study. Patients between 18 and 65 years old with chronic neck pain were eligible to participate if they had disability levels at least 5 out of 50 on the Neck Disability Index. Patients were divided into three groups: Group A received deep neck flexor and home-based exercises; Group B received superficial muscle and home-based exercises; and Group C received home-based exercises only. The Numeric Pain Rating Scale (NPRS), Neck Disability Index, and Hospital Anxiety and Depression Scale were administered at baseline and 7 weeks later. RESULTS: The highest improvements in pain intensity levels were observed in Group A with 4.75 (1.74) NPRS points, and the lowest were in Group C with 1.00 (1.10). The highest reductions in anxiety and depression levels were noted in Group A (2.80) and Group B (1.65), respectively. The highest improvements in pain intensity levels were observed among Groups A versus C with 2.80 (0.52) NPRS. The highest reductions in anxiety and depression levels were noted among Groups A versus C with 1.75 (1.10) points and Groups B versus C with 1.60 (0.90) points, respectively. CONCLUSIONS: Deep and superficial flexor muscle training along with home-based exercises is likely to reduce chronic neck pain and anxiety/depression levels by a clinically relevant amount. Future larger scaled randomized controlled trials are warranted to further support these findings.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".