Investigating women’s chest size, trunk muscle co-contraction and back pain during prolonged standing
Bibliographic record
Abstract
BACKGROUND: Chest size is a known factor in the development of back pain for women. However, the neuromuscular mechanisms associated with chest size and back pain are poorly understood. OBJECTIVE: The purpose of this study was to investigate chest size and its association with back pain development and muscle activity patterns during prolonged standing. METHODS: Twenty university-aged women were divided into two groups: small chest size (n= 10, ∼A/C cup) and large chest size (n= 10, ∼D/E cup). Participants completed a 2-hr standing protocol, where eight channels of bilateral trunk electromyography were collected. Muscle activity, specifically co-contraction, was compared between chest size groups, pain developers, and time. RESULTS: The large chest size group reported higher amounts of pain at the upper, middle, and low back. Women in the large chest group sustained higher levels of co-contraction for muscles involving the thoracic and lumbar erector spinae compared to those in the small chest size group during prolonged standing. CONCLUSIONS: Thoracolumbar co-contraction determined in this study may be a potential mechanism contributing to increased back pain development for women with large chest sizes during prolonged standing. This pain mechanism could be targeted and addressed in future non-invasive musculoskeletal rehabilitation to improve back pain for women.
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 | 0.001 |
| 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) | 0.002 | 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".