A Cross-Sectional Analysis of Pain, Neck Disability, Functional Performance, and Quality of Life in Patients with Cervical Spondylosis
Bibliographic record
Abstract
Background/Objectives: Cervical spondylosis is a cause of recurrent neck pain, disability, and poor quality of life (QoL). This study aimed to examine the relationships among pain intensity, neck disability, functional performance, body mass index, and quality of life in individuals with cervical spondylosis. Methods: The study was conducted as a cross-sectional study on 111 participants (33 males and 78 females). Data were collected using different assessment tools such as Neck Disability Index (NDI), Numeric Rating Scale (NPRS) for pain, Patient-Specific Functional Scale (PSFS), and McGill Quality of Life Questionnaire (Part A). Independent t-tests and ANOVA assessed group differences, whereas correlations and multiple linear regression examined the association of QoL and function. Data from 107 out of 111 participants were further analysed due to missing data. Results: No significant gender- or activity-based differences were observed for pain, disability, function, or QoL (p > 0.05). However, negative correlations were found to be significant between NDI and both PSFS (r = −0.41, p < 0.001) and QoL (r = −0.52, p < 0.001). Regression analysis identified NDI, pain intensity, BMI, and PSFS as significant independent correlates of QoL (Adj. R2 = 0.426, p < 0.001), although BMI alone was associated with functional ability (Adj. R2 = 0.141, p = 0.008). Higher neck disability, pain, and BMI were associated with poorer functional and QoL outcomes. Functional ability occurred as a positive determinant of QoL. Conclusions: These results highlight the need for integrated management focusing on pain reduction, functional rehabilitation, and weight optimisation to improve quality of life in patients with cervical spondylosis.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".