Bibliographic record
Abstract
1This is best demonstrated in athletes, a population of patients at greatest risk for repeated head injuries. In fact, concussions are the most common head injury sustained by athletes; 8.9% of all high school sports injuries reported are concussions and account for 19% of all non-fatal injuries in football. 2 The incidence of concussion among American teen athletes has grown from 300,000 incidents annually 10 years ago to upward of three million cases now. The increase is likely due to the increased awareness by the sports community, leading to greater recognition and reporting. It is unclear if changes in rules and protective equipment has changed incidence. Nonetheless, these figures underestimate the frequency of concussions, as those with minor head injuries are often unlikely to seek care. In a survey by the Associated Press in 2009, 3 it was found that at the professional level, nearly one-fifth of 160 NFL players had hidden or downplayed the effects of their concussions. Athletes fear being removed from play and letting teammates down. Coaches, sideline personnel, and athletes themselves often do not recognize their own symptoms as a concussion. According to a McGill University study, 70.4% of athletes surveyed retrospectively reported experiencing the symptoms of a concussion during the past year, but only 23.4% realized that they had sustained a concussion in real time. 4 The study also found that 84.6% of athletes with a concussion had actually experienced more than one concussion. Part of the dilemma in diagnosing concussions is that the definition itself has been evolving. At this time, the most accepted definition of con cussion is a clinical one, introduced in 2001. 5
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.030 | 0.021 |
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".