Economic impact of work disability due to chronic low back pain from the patient perspective
Bibliographic record
Abstract
OBJECTIVES: Low back pain (LBP) is one of the main expenditure items for health systems. Data on the economic impact of LBP are uncommon from the patient perspective. The aim of this study was to estimate the economic impact of work disability related to chronic LBP from the patient perspective. METHODS: We conducted a cross-sectional analysis from patients aged over 17 years suffering from non-specific LBP for at least 3 months. Systematic medical, social and economic assessments were collected: pain duration and intensity; functional disability with the Quebec Back Pain Disability Scale (0-100); quality of life with the Dallas Pain Questionnaire; job category; employment status; duration of work disability due to LBP, and income. Factors associated with loss of income were identified by multivariable logistic regression analysis. RESULTS: We included 244 workers (mean age 43 ± 9 years; 36% women); 199 patients had work disability, including 196 who were on sick leave, 106 due to job injury. Three were unemployed due to layoff for incapacity. The mean loss of income for patients with work disability was 14% [SD 24, range -100 to 70] and was significantly less for patients on sick leave due to job injury than on sick leave not related to job injury (p < 0.0001). On multivariable analysis, the probability of loss of income with LBP was about 50% less for overseers and senior managers than workers or employees (odds ratio 0.48 [95% confidence interval 0.23-0.99]). CONCLUSION: Work disability due to LBP resulted in loss of income in our study. The loss of income depended on the type of social protection and job category. It was reduced for patients on sick leave related to work injury and for overseers and senior managers.
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 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".