A - 29 Language Differences in Neurocognitive Scores and Symptom Report in Professional Football Players
Bibliographic record
Abstract
Abstract Objective The purpose of this study was to investigate the role of language on neurocognitive test outcomes and concussion symptom ratings in professional football players. Methods Design/Setting - A retrospective cross-section analysis of 1546 male Canadian Football League (CFL) athletes was conducted using baseline data collected from the Immediate Post-Concussion Assessment and Cognitive Test (ImPACT) across the 2016–18 competitive seasons. Independent Variables - Participants (1546) were divided into three language categories, native English-speaking, bilingual – whose first language was English, and English as a second language (ESL). Years of education, age, and concussion history were entered as co-variates. Outcome Measure -The 5 Composite scores from ImPACT and the 22 symptoms from the post-concussion symptom scale (PCSS). Results Results of the MANCOVA showed no significant differences between language groups on any of the five ImPACT composite scores F(10, 3072) = 1.09, p = 0.36. The Kruskall-Wallis test revealed significant differences were found in three symptoms including dizziness [X2(2, 1486) = 32.85, p < 0.001)], sadness [X2(2, 1486) = 6.505, p = 0.04], and concentration [X2(2, 1486) = 11.01, p = 0.004)] with the bilingual and non-native English speakers having higher scores. Conclusions This study suggests that cultural and linguistic differences should be considered when administering CNTs. While differences in cognitive outcomes have not been consistently found across studies, differences in baseline symptom reports have been consistently demonstrated. Information pertaining to a patient’s level of acculturation and language proficiency is important for examiners when working with diverse populations. Continuing to develop language-specific normative databases is encouraged.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".