Alterations in Cervical Nerve Root Function during Different Sitting Positions in Adults with and without Forward Head Posture: A Cross-Sectional Study
Bibliographic record
Abstract
The current study aimed to determine whether participants with and without forward head posture (FHP) would respond differently in cervical nerve root function to various sitting positions. We measured peak-to-peak dermatomal somatosensory-evoked potentials (DSSEPs) in 30 participants with FHP and in 30 participants matched for age, sex, and body mass index (BMI) with normal head posture (NHP), defined as having a craniovertebral angle (CVA) >55°. Additional inclusion criteria for recruitment were individuals between the ages of 18 and 28 who were in good health and had no musculoskeletal pain. All 60 participants underwent C6, C7, and C8 DSSEPs evaluation. The measurements were taken in three positions: erect sitting, slouched sitting, and supine. We identified statistically significant differences in the cervical nerve root function in all postures between the NHP and FHP groups (p < 0.001), indicating that the FHP and NHP reacted differently in different positions. No significant differences between groups for the DSSEPs were identified for the supine position (p > 0.05), in contrast to the erect and slouched sitting positions, which showed a significant difference in nerve root function between the NHP and FHP (p < 0.001). The NHP group results were consistent with the prior literature and had the greatest DSSEP peaks when in the upright position. However, the participants in the FHP group demonstrated the largest peak-to-peak amplitude of DSSEPs while in the slouched position as opposed to an erect position. The optimal sitting posture for cervical nerve root function may be dependent upon the underlying CVA of a person, however, further research is needed to corroborate these findings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".