Previous history of LBP with work loss is related to lingering deficits in biomechanical, physiological, personal, psychosocial and motor control characteristics
Bibliographic record
Abstract
A cross-sectional retrospective study was made of currently asymptomatic workers who perform physically demanding jobs. To further quantify the association between various biomechanical, physiological, personal psychosocial and motor control parameters that linger due to a history of low back disorders. Seventy-two workers were recruited from heavy industry, 26 of whom had a history of disabling low back disorders (LBDs) sufficient to miss work while the others did not. The strength of the study lies in the many detailed variables measured. Having a history of low back disorders was found to be associated with a larger waist girth, a greater potential for low back pain chronicity as predicted from psychosocial questionnaires, perturbed flexion to extension strength and endurance ratios, and widespread motor control deficits across a variety of tasks, some of which resulted in high back loads. In those workers who had missed work due to back disorders, the length of time since their last disabling episode was 261 weeks on average, suggesting that multiple deficits may remain for a period of time. Having a history of LBD is associated with changes in attitudes, in body composition, and in the way people move, load their backs and respond to a variety of motor and stability challenges.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".