Efficacy Of Soft-shell Padding On Head Impact Kinematics In American Football: Pilot Findings
Bibliographic record
Abstract
PURPOSE: Soft-shell padding can be used to augment standard football helmets and is purported to reduce head impact severity. We aimed to determine if soft-shell padding reduces head impact kinematics a) among individuals that had not previously worn soft-shell padding, and b) between teammates with or without soft-shell padding. METHODS: We studied 15 offensive and defensive linemen and linebackers completing the 2021 fall college season. This included a group of players wearing soft-shell protective padding for the final 5 weeks of the season (SHELL; n = 10; height = 194.0 ± 3.6 cm; mass = 134.3 ± 10.6 kg), and a control group not wearing soft-shell padding (CONTROL; n = 5; height = 188.0 ± 6.5 cm; mass = 119.1 ± 12.1 kg). Helmets were instrumented with Head Impact Telemetry System to quantify peak linear (g) and rotational (rad/s2) accelerations. Among SHELL, we conducted a Wilcoxon signed-rank test to compare head impact kinematics between weeks 1-7 (without soft-shell padding) and weeks 8-12 (with soft-shell padding). We also compared SHELL and CONTROL groups with Mann-Whitney U-Tests across these outcomes during weeks 8-12. RESULTS: Two head impacts from SHELL and seven impacts from CONTROL were statistical outliers (linear acceleration > 80 g) and removed from dataset. Within SHELL, there were no significant differences (Z = -.096; p = .924) in linear acceleration between weeks 1-7 (n = 2176; median [interquartile range]: 21.8 [14.2] g and weeks 8-12 (n = 755; 22.1 [15.3] g). A similar finding was observed for rotational acceleration (Z = .354, p = .552; weeks 1-7: 1464.2 [922.9] rad/s2; weeks 8-12: 1478.7 [1153.3] rad/s2). There were no differences in linear acceleration (U = 1.926, p = 0.185) during weeks 8-12 between SHELL (n = 755; 20.8 [14.0] g) and CONTROL (n = 392; 22.1 [15.3] g) groups. We observed a similar trend for rotational acceleration (U = 0.073, p = 0.835; SHELL: 1493.2 [1014.5] rad/s2; CONTROL: 1478.7 [1153.3] rad/s2). CONCLUSIONS: Based on the findings of our pilot study, soft-shell padding did not reduce head impact severity. Future studies should consider video-confirmed impacts to examine practice characteristics (individual vs team drills) to further evaluate the football specific contexts in which soft-shell protective padding may best reduce head impact burden for athletes who choose to use them.
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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 | 0.000 | 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".