Video analysis of potential concussions in elite male Hurling: are players being assessed according to league guidelines?
Bibliographic record
Abstract
BACKGROUND: Hurling is a fast-paced contact sport that places players at risk of concussion. Given the consequences of repeated concussive impacts, it is imperative that concussion management guidelines are followed. HYPOTHESIS/PURPOSE: The aim of this study is to determine if potential concussive events (PCEs) in elite Hurling are assessed in accordance with league management guidelines. The secondary objective is to investigate the effectiveness of current concussion training programs. METHODS: Investigators used a video analysis approach to identify PCEs throughout the 2018 and 2019 inter-county Hurling seasons and championships. Subsequent assessment, return to play (RTP) decision, and signs of concussion were evaluated based on previously validated methods. The results were then compared year-over-year with previous research in Gaelic Football (GF). RESULTS: A total of 183 PCEs were identified over 82 matches. PCEs were frequently assessed (86.3%, n = 158) by medical personnel. The majority of assessments were less than 1 min in duration (81.0%, n = 128). Thirteen (7.1%) players were removed following a PCE. There were 43 (23.5%) PCEs that resulted in one or more signs of concussion, of which 10 (23.3%) were removed from play. There was no difference in rate of assessment, duration of assessment, or rate of RTP between 2018 and 2019 in both Hurling and GF, suggesting that current concussion training programs have had limited success. CONCLUSION: In Hurling, players suspected of having sustained a concussion are frequently subject to a brief assessment, and are rarely removed from play. Affirmative action is needed to ensure the consistent application of standardized concussion assessment across the Gaelic Games.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".