Assessment of trunk muscle co-contraction during typical occupational movement tasks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Assessment of trunk muscle co-contraction can provide insight into the behaviour of the trunk musculature, as co-contraction differs between healthy participants and those with low back pain/injury. To date, co-contraction of the trunk musculature has been examined predominately during single-plane and maximal range-of-motion movement tasks. Objective: To assess differences in co-contraction patterns of the trunk musculature as a function of movement task (maximal and combined, mid-range trunk movement tasks) and phase of the movement task. Methods: Thirteen asymptomatic males performed a series of maximal trunk range-of-motion tasks, as well as movement tasks with various combinations of lumbar and thoracic movements ('combined' movement tasks), in both sitting and standing. Co-contraction between all possible pairings of six bilateral muscles (66 in total) was determined and compared between movement tasks and phase of movement. Results: Twisting and combined movement tasks produced greater co-contraction when moving into and/or holding the position, while uncontrolled flexion movement tasks produced the greatest co-contraction when returning to a neutral upright position. Conclusions: Combined movement tasks and tasks involving twisting required greater co-contraction to actively maintain the positions, providing insight into potential mechanisms of injury if the positions were adopted with high repetition or long duration. These findings are applicable to injury prevention, job and workstation design, rehabilitation practices, and return-to-work protocols.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it