Determining whether biomechanical variables that describe common ‘safe lifting’ cues are associated with low back loads
Bibliographic record
Abstract
Lift technique training programs have been implemented to help reduce injury risk, but the underlying content validity of cues used within these programs is not clear. The objective of this study was to determine whether biomechanical variables, that commonly used lifting cues aim to elicit, are associated with resultant low back extensor moment exposures. A sample of 72 participants were recruited to perform 10 repetitions of a floor-to-waist height barbell lift while whole-body kinematics and ground reaction forces were collected. Kinematic, kinetic, and energetic variables representative of characteristics commonly targeted by lifting cues were calculated as predictor variables, while peak and cumulative low back moments were calculated as dependent measures. Multiple regression revealed that 56.6-59.2% of variance in low back moments was explained by predictor variables. From these regression models, generating motion with the legs (both greater hip and knee work), minimizing the horizontal distance of the body to the load, maintaining a stable body position, and minimizing lift time were associated with lower magnitudes of low back moments. These data support that using cues targeting these identified variables may be more effective at reducing peak low back moment exposures via lift training.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".