Effects of Unexpected Lateral Mass Placement on Trunk Loading in Lifting
Bibliographic record
Abstract
STUDY DESIGN: A repeated measurements experiment of spinal loading in healthy subjects. OBJECTIVES: To test whether unexpected lateral mass placement increases low back loading and trunk movement when subjects are lifting a mass in upright posture. SUMMARY OF BACKGROUND DATA: Epidemiologic studies suggest that sudden, unexpected loading will lead to low back pain. Also, asymmetric loading is considered to be harmful to the spine. It can be anticipated that unexpected asymmetric loading will increase the risk of injury even more. METHODS: Ten subjects lifted in an upright posture a crate, in which a mass of 10 kg was placed laterally at the left side either expectedly or unexpectedly. The crate reaction forces, body movements, and trunk muscle activity were measured. From these, the L5-S1 net moments and muscle forces were estimated. RESULTS: Unexpected lateral placement of the mass caused no clear increase in peak low back loading. The stiffness of the trunk was lower in the unexpected condition, which, in combination with inadequate net moments produced, resulted in movement of the trunk to the side of the displaced mass. CONCLUSIONS: Unexpected lateral mass placement does not increase the compression force. Perturbed trunk movement and lower muscle forces indicated a decreased stability of the spine, which may imply an injury risk.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".