EFFECTS OF CERVICAL TRACTION AND INFRARED THERAPY ON PAIN INTENSITY AND NECK DISABILITY INDEX AMONG PEOPLE WITH CERVICAL SPONDYLOSIS: A CROSS-OVER COHORT STUDY
Bibliographic record
Abstract
Objective: To investigate the effects of six-week cervical traction and infrared therapy on neck pain intensity (PI) and neck disability index (NDI) among individuals with cervical spondylosis. Methods: This study was a non-randomized cross-over cohort design. A total of 40 participants (men=20, and women=20) who were purposively selected, received a six-week infrared therapy, observed a one-week washout period, and then six-week concurrent infrared therapy and cervical traction. The PI and NDI were measured at baseline, after infrared therapy, the washout period, and infrared plus cervical traction. Data were analyzed using repeated-measures analysis of variance (RM ANOVA), Friedman’s ANOVA, independent samples t-test, and Kendall’s tau correlation test. Result: The mean age of the participants was 40±8.60 years. Infrared therapy plus cervical traction significantly reduced PI: [Formula: see text] 2 (3)=102.06, [Formula: see text], and NDI: F (1, 39) = 222.56, [Formula: see text], relative to infrared therapy alone. Specifically, the minimum clinically important difference (MCID) for PI was 2.2, while infrared alone reduced the PI by 1.0 (Z = 4.633, [Formula: see text]), infrared therapy plus cervical traction reduced PI by 6.0 (Z = 7.405, [Formula: see text]). The MCID for NDI was 8.50, while infrared alone reduced the NDI by 1.05 (t = 30.087, [Formula: see text]), infrared therapy plus cervical traction reduced NDI by 15.83 ([Formula: see text], [Formula: see text]). Conclusion: Concurrent infrared and cervical traction significantly reduced PI and NDI among patients with cervical spondylosis more than lone infrared therapy.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".