Recruitment order of the abdominal muscles varies with postural task
Bibliographic record
Abstract
Abdominal muscle recruitment strategies in response to a postural perturbation contradict the theory that the deeper abdominal muscles are always recruited in advance of the more superficial muscles. The purpose of this study was to determine whether such contrasting muscle recruitment patterns are due to the postural task or the predictability of a postural task. Participants performed an arm raise task as well as an unpredictable and a predictable balance perturbation task (i.e. support-surface translation) while intramuscular electromyographic (EMG) recordings were obtained from the deep [transversus abdominis (TrA)] and superficial [obliquus externus (OE)] abdominal muscles. The abdominal muscle recruitment order was dependent on the postural task but not on the predictability of a postural perturbation. Whereas arm raises elicited similar EMG onset latencies in TrA and OE, the OE onset latency was 48 ms earlier than the TrA following an unpredictable translation (P = 0.003). The early OE activation persisted when the translation was made predictable to the participant (P = 0.024). These results provide evidence that the abdominal muscle recruitment order varies with the trunk stability requirements specific to each task. Rehabilitation strategies focusing on an early TrA activation to improve postural stability may not be appropriate for all everyday tasks.
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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 | 0.000 | 0.003 |
| 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) | 0.001 | 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".