Prevalence of fatigue and associated factors in chronic low back pain patients
Bibliographic record
Abstract
OBJECTIVES: to determine the prevalence and key factors associated with fatigue in chronic low back pain patients. METHODS: cross-sectional study of 215 chronic low back pain patients from three health care centers and two industrial corporations. The crude prevalence of fatigue and its 95% confidence interval (CI) were calculated. Associations between fatigue and the independent variables were measured. RESULTS: the prevalence of fatigue among the participants was 26.0% [95% CI: 20.3-32.5]. Fatigue was independently associated with depression and self-efficacy. An increase of one unit in the score of depression increased the risk of fatigue by 9%; an increase of one unit in the score of self-efficacy reduced the risk of fatigue by 2%. CONCLUSIONS: fatigue was prevalent in chronic low back pain patients and associated with depression and self-efficacy. Knowing these factors can direct strategies for prevention and control of fatigue in chronic low back pain patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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 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".