Bibliographic record
Abstract
Introduction: Mild Brain Injury or concussion is frequently observed during contact sports such as football and soccer. If it remains undetected, a repeat concussion can lead to long term consequences because of the vulnerable brain tissue damage. Balance Error Scoring System (BESS) test is a widely used test to detect concussion. It requires the athlete with suspected concussion to maintain his/her position in three different stances, that are, both legs, single leg and tandem, and has a total score ranging from 0 to 30. The recommended way of performing this test is to do it barefoot in clinical or semi-clinical settings. Such conditions are however difficult to achieve during an ongoing match, and athletes would like to be near the field with their cleats on - situations often found on soccer fields in eastern countries. Objective: Therefore, we aimed to assess the reliability of BESS test in different field conditions. This research is in line with Qatar National Research Strategy 2012 pillars, H.E.1.9 (prevention of brain Injury) and H.E 1.10 (control of sports injuries). Methods: This study was conducted under the auspices of McGill Sports Emergency Medicine Clinic. Athletes from soccer and football teams were approached on the field during practice games. After informed consents, they performed BESS test in three conditions, that were, barefoot, on turf with cleats and on hard surface with cleats. Each athlete was rated by three observers independently of each other. We computed mean difference in total BESS scores with 95% confidence intervals (95%CI). Comparison of total BESS scores under different conditions as well as inter-observer reliability was assessed using intraclass correlation coefficient (ICC).. Results: We recruited 49 athletes from football (n=39) and soccer (n=10) teams in this study. Thirty nine of them were male, 10 were females. Average age was 21.1 years (standard deviation [SD]=1.9). We found that total BESS scores were significantly different (P<0.001) between barefoot and the two conditions with cleats-on: 2.2 (95%CI=1.6, 2.8) for turf and 2.0 (95%CI=1.4, 2.6) for hard surface. Concordances of barefoot with turf (ICC=0.47, P=0.02) and hard surface (ICC=0.51, P=0.01) conditions were moderate. A moderate to high inter-observer reliability (0.60≥ICC≤0.75) was observed for BESS test under three conditions. Conclusion: These findings show that BESS test has a fair reliability under different conditions, and may be useful in screening concussion on the field. However, cut-off of BESS should be reduced by 1 to 2 points if it is applied on the field with cleats.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".