Ten years later: Ice hockey helmet impact mechanics change with age
Bibliographic record
Abstract
This longitudinal 10-year study investigated the effects of inventory aging on ice hockey helmets’ impact attenuation characteristics. Three unused helmet models with different foam padding materials (vinyl nitrile, multi-density vinyl nitrile, and expanded polypropylene) were impact tested at six sites around a surrogate headform on years 2, 6, and 10 (Y2, Y6, and Y10) after the date of manufacture. In general, peak acceleration (g) Y10 measures were greater than those reported in Y2 and Y6, although well below standard impact criteria levels. Visual inspection of helmets post-impact showed no conspicuous damage to liner or shell, although in several instances the binding glue had disintegrated allowing liners to shift or fall away from the shell. In summary, contemporary ice hockey helmets retain most of their robust impact attenuation characteristics 10 years in storage after manufacture date; however, adhesive tearing of padding from the shell needs to be addressed. Regular inspection of the helmet integrity by players, coaches, and trainers is paramount. Further testing of used helmets in a similar prospective manner is carried out to ensure safe helmet function.
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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".