Concussions in Sledding Sports and the Unrecognized “Sled Head”: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Sport-related concussion is a significant public health concern. Little research has been conducted on sport-related concussion and injury prevention strategies in competitive sledding sports like bobsleigh, luge, and skeleton. Athletes have identified ‘sled head’ as a key concern due to its symptom burden. PURPOSE: To summarize our knowledge of the prevalence of concussion and related symptoms in sledding sports; to utilize Haddon's Matrix to inform and define strategies for injury prevention. METHODS: An independent information specialist conducted a search for the known literature on injuries in non-recreational sledding sports, and specifically for concussion via OVID Medline, CINAHL, the Cochrane Database, EMBASE, PsycInfo, PubMed, Scopus, and the Web of Sciences from 1946 to December 2017. After iterative searches of reference sections, a total of 844 articles were assessed for inclusion. RESULTS: Nine articles were included for review. Concussions are a common occurrence in elite sledding sport athletes, affecting 13-18% of all sledding athletes. Significant variance exists between events, indicating a potential effect of the ice track in injury risk. The condition known as ‘sled head’ is discussed and identified as a key point of further investigation. A number of potential injury prevention strategies are discussed. INTERPRETATION: Head injuries and concussions are an important injury for elite sledding sports and a number of avenues exist for prevention. More work is required to delineate the mechanisms, characteristics, natural history and management of ‘sled head’.
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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".