Mild traumatic brain injury after traffic collisions: a population-based inception cohort study
Bibliographic record
Abstract
OBJECTIVE: To study the incidence and claim closure of traffic-related mild traumatic brain injury and the effect of insurance factors. DESIGN: Population-based, cohort study of mild traumatic brain injury caused by traffic collisions in Saskatchewan, Canada, between July 1, 1994 and December 31, 1995. On January 1, 1995 the insurance law changed from tort to no fault. SUBJECTS: 657 adults, 18 years or older, who hit their head and indicated loss of consciousness or uncertain loss of consciousness and were not hospitalized for more than 2 days. METHODS: Subjects entered the cohort on the injury date and exited on the day the insurance claim closed, or on November 1, 1997, when remaining open claims were censored. All 657 subjects answered a baseline questionnaire, and 479 who did not reopen their claim were included in the follow-up. The relationship between claim closure and health was studied in 225 (47%) of these claimants. RESULTS: The 6-month incidence dropped from 36/100,000 to 27/100,000 after the insurance change. The median time-to-claim closure dropped from 408 days to 233 days. Prolonged claim closure was associated with both injury and insurance-related factors. Claim closure occurred faster when claimants' health improved. CONCLUSIONS: Mild traumatic brain injury incidence and claim closure is affected by both health and insurance-related factors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".