Lumbar supports to prevent recurrent low back pain among home care workers: A randomized trial
Bibliographic record
Abstract
BACKGROUND: People use lumbar supports to prevent low back pain. Secondary analyses from primary preventive studies suggest benefit among workers with previous low back pain, but definitive studies on the effectiveness of supports for the secondary prevention of low back pain are lacking. OBJECTIVE: To determine the effectiveness of lumbar supports in the secondary prevention of low back pain. DESIGN: Randomized, controlled trial. SETTING: Home care organization in the Netherlands. PATIENTS: 360 home care workers with self-reported history of low back pain. INTERVENTION: Short course on healthy working methods, with or without patient-directed use of 1 of 4 types of lumbar support. MEASUREMENTS: Primary outcomes were the number of days of low back pain and sick leave over 12 months. Secondary outcomes were the average severity of low back pain and function (Quebec Back Pain Disability scale) in the previous week. RESULTS: Over 12 months, participants in the lumbar support group reported an average of -52.7 days (CI, -59.6 to -45.1 days) fewer days with low back pain than participants who received only the short course. However, the total sick days in the lumbar support group did not decrease (-5 days [CI, -21.1 to 6.8 days]). Small but statistically significant differences in pain intensity and function favored lumbar support. LIMITATIONS: Study participants were unblinded, and a substantial amount of missing data required imputation. Objective data on sick days due to low back pain were not available. CONCLUSION: Adding patient-directed use of lumbar supports to a short course on healthy working methods may reduce the number of days when low back pain occurs, but not overall work absenteeism, among home care workers with previous low back pain. Further study of lumbar supports is warranted.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".