Age, Gender and Mechanism of Injury Interactions in Post-Concussion Syndrome
Bibliographic record
Abstract
BACKGROUND: Certain factors such as age and gender seem to affect the risk of developing post-concussion syndrome (PCS). We assessed the interactions between age, gender, concussion history and mechanism of injury in PCS patients so that a better understanding could guide the development of targeted prevention strategies. METHODS: Demographic data including age, gender, concussion mechanism of injury and concussion history were collected from (1) a prospective study evaluating PCS biomarkers and (2) a retrospective chart review of PCS patients. A total of 437 PCS patients who were assessed at the Canadian Concussion Centre or Toronto Western Hospital, Toronto, ON, were included. RESULTS: Overall, there were more men with PCS; however, a greater percentage of women had PCS after a single concussion. The results showed that age, gender and concussion history are conditionally dependent on the mechanism of injury, and independent of one another. The relative frequency of having PCS was greater in the following instances: (1) being a woman and having had concussion from a fall or motor vehicle collision (MVC), (2) being older and having had concussion from a fall or MVC or (3) having a single concussion with cause being MVC or fall. CONCLUSION: In patients with PCS, age and gender interact with the mechanism of injury to influence the risk of concussion. Targeted prevention strategies may be essential to prevent injuries leading to PCS.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".