Bibliographic record
Abstract
The images of Olympic athlete Nodar Kumaritasvili flying off his luge and contacting a fixed steel beam at the recent Winter Olympics in Vancouver, British Columbia, were shocking. But the resultant injuries and death of the young athlete were not surprising to EMS personnel who witnessed the incident, because training and experience with similar mechanisms of injury intuitively teach us that these types of patients are susceptible to traumatic brain injury (TBI). Worldwide, TBI is the leading injury cause of death and permanent disability. In the U.S. alone, 1.4 million cases of TBI present to emergency services every year. Many more cases go unreported and untreated. These TBIs lead to 235,000 hospitalizations and, ultimately, 50,000 deaths.(1) For instance, blunt trauma alone kills 1% of those affected, but when a TBI is also involved, the mortality rate increases to 30%.(2) Some 50% of those who die from TBI do so within the first two hours of injury, making emergent prehospital intervention critical.(3) Preventing secondary injury by proper prehospital management can save brain function and lives.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.996 | 0.996 |
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; both teacher heads agree on what is shown here.
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".