Role of Primary Care Optometrists in the Assessment and Management of Patients with Traumatic Brain Injuries in Canada
Bibliographic record
Abstract
Traumatic brain injury (TBI) results from a strong blow or jolt to the head that disrupts the normal function of the brain.1 The severity of a TBI can range from mild to severe, depending on the patient’s mental status, con-sciousness level and amnesia following the injury. The annual incidence of TBI in North America and Europe is conservatively estimated to be ap-proximately 600/100,000.2,3 This translates to at least 200,000 TBI cases in Canada every year. According to the Centers for Disease Control and Prevention, and the Canadian Institute for Health Information, the leading cause of TBIs that result in hospital admission is falls (35%-45%), followed by motor vehicle accidents (17%-36%), collision-related events (struck by or against) (10-17%) and assaults (9-10%).4,5 Head injuries are more common in the 0- to 19-year age group, followed by those who are aged 60+. Males are more highly represented in every age group than females. However, it should be noted that the demographics of patients who present in an op-tometrist’s office may differ from those based on hospital admissions [...]
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 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".