Predictive Contribution of the Superficial Neck Muscles to Short-Latency Rate of Force Development of the Head and Neck
Bibliographic record
Abstract
PURPOSE: To evaluate the contribution of splenius capitis, sternocleidomastoid, and upper fibers of trapezius activation to the gains in rate of force development (RFD) of the head and neck during maximum voluntary ballistic contractions. METHODS: RFD gain was facilitated by a single-session intervention for maximum voluntary ballistic contractions in the anterior direction, oriented at 45° to the midsagittal plane, which require active restraint of axial rotation. Muscle activation for the agonist (sternocleidomastoid) and 2 antagonists (splenius capitis and upper fibers of trapezius) was evaluated. The study sample included 12 physically active men (mean age, 22.6 y). RFD (N·m·s-1; 0-100 ms) and integrated muscle activity (50 ms before and 100 ms after force onset) were measured at 10 minutes, 20 minutes, and 2 days postintervention, relative to baseline. Muscle activation predictive of RFD gains was evaluated by linear regression analysis. RFD reproducibility was evaluated using the coefficient of variation of the typical error. RESULTS: The intervention yielded a 1.95- to 2.39-fold RFD gain (P ≤ .05), with greater RFD gain for participants with a lower peak moment of force (<10.9 N·m) than those with a higher peak moment (≥10.9 N·m) at baseline (P ≤ .002). For the low group, 65% to 74% of the RFD gain was predicted by ipsilateral sternocleidomastoid activation, with ipsilateral splenius capitis activation predicting 77% to 92% of RFD gain for the high group. Absolute peak and impulse of static force were greater for the high than for the low group (P ≤ .04). RFD reproducibility was high (coefficient of variation of the typical error ≤ 14.4%). CONCLUSIONS: The agonist- and antagonist-focused synergies might reflect different functional priorities, higher RFD gain compared with higher head-neck force.
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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 | 0.001 | 0.004 |
| 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".