Sex differences in upper limb 3D joint contributions during a lifting task
Bibliographic record
Abstract
Sex-related differences in work technique may contribute to increasing the risk of musculoskeletal joint disorders among women. In lifting tasks, sex differences have been reported for the trunk and lower limb, although women present a higher prevalence of shoulder disorders. We investigated sex differences in the upper limb technique during a lifting task. Trunk and upper limb kinematics were recorded in 27 women and 27 men lifting a box (6 or 12 kg) from hip to eye level. Work technique was quantified through the three-dimensional contribution of each joint to overall box height. The glenohumeral joint showed a higher contribution in women with a 6 kg box and wrist and elbow joints did with a 12 kg box, compared to men at either 6 or 12 kg. Sex differences occurred systematically above shoulder level. Our results argue for careful consideration of sex during ergonomic intervention, particularly during the overhead task.Practitioner Summary: We investigated the sex-related differences in upper limb technique during lifting tasks. Results highlight a sex-specific kinematic strategy above the shoulder level on the glenohumeral joint and on the wrist and elbow joints. To help reduce women’s shoulder disorders in overhead task, ergonomic interventions should account for those differences.Abbreviations: DoF: degree-of-freedom; WR/EL: wrist and elbow; GH: glenohumeral; SC/AC: sternoclavicular and acromioclavicular; TR/PE: pelvo-thoracic
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.006 | 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".