The Role Of Sex As A Moderator Of Cognitive Outcome Following A Sport-related Concussion
Bibliographic record
Abstract
Despite increasing research, results regarding sex as a moderator of outcome following a concussion are conflicting and management guidelines remain uncertain. PURPOSE: To determine if there is a gender difference in the long-term cognitive outcomes of athletes with a history of concussion (HOC). METHODS: 196 collegiate athletes (49 HOC women, HOC 49 men, 49 women matched controls, 49 men matched controls) completed a modified Cogstate test battery, to which a 2-back condition (N-back task) was added to increase our ability to detect persistent deficits in higher cognition. All participants were symptom-free at time of testing and those with a HOC were 6+ months from injury (24.0 ± 15.8 months, 1.44 ± 1.3 prior concussions). RESULTS: A significant interaction of error type × HOC × sex was observed. Specifically, HOC men exhibited a greater number of omission errors relative to HOC women (2.94 ± 2.5 vs. 0.31 ± 0.9, p<0.05). Moreover, irrespective of sex, HOC athletes exhibited a greater number of errors on the 1-back (2.30 ± 1.5 vs. 0.79 ± 0.9, p<0.01) and 2-back conditions (3.34 ± 2.6 vs. 1.46 ± 1.4, p<0.01), relative to controls. HOC athletes also exhibited decreased accuracy on the 1-back (95.45 ± 3.0% vs. 98.21± 2.2%, p<0.01) and 2-back conditions (93.98 ± 4.1% vs. 96.65 ± 3.3%, p<0.05). CONCLUSIONS: The current results suggest that beyond the acute phase of the injury, sex does not seem as a moderating variable of cognitive outcomes following concussion. Furthermore, the results reaffirm that concussive injuries can result in persistent deficits in aspects of higher cognition.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.000 |
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".