Medical Care Among Individuals with a Concussion in Ontario: A Population-based Study
Bibliographic record
Abstract
BACKGROUND: Concussion affects 1.2% of the population annually; rural regions and children have higher rates of concussion. METHODS: Using administrative health care linked databases, all residents of Ontario with a physician diagnosed concussion were identified using ICD-9 code 850 or ICD-10 code S06. Cases were tracked for 2 years for concussion-related health care utilization with relevant specialist physicians (i.e., neurology, otolaryngology, physiatry, psychiatry, ophthalmology). Billing codes, specialist codes, and time from index to visit were analyzed. Factors associated with increased specialist visits were also examined. RESULTS: In total, 1,022,588 cases were identified between 2008 and 2014 with 2 years of post-concussion health care utilization available. Follow-up by physician within 3 days of injury occurred in only 14% of cases. Mean time between ED diagnosis and follow-up by a physician was 83.9 days, whereas for rural regions it was >100 days. About half of adults (51.9%) and children (50.3%) had at least 1 specialist visit following concussion. Mean time between injury and first specialist visit was 203.8 (SD 192.9) days for adults, 213.5 (SD 201.0) days for rural adults, and 276.0 (SD 202.6) days for children. There were 67,420 neurology visits, 70,404 psychiatry visits, 13,571 neurosurgery visits, 19,780 physiatry visits, 101,788 ENT visits, and 103,417 ophthalmology visits in the 2 years tracking period. Factors associated with more specialist use included age > 18 years, urban residence, and pre-injury psychiatric history. CONCLUSIONS: There are discrepancies in post-concussion health care utilization based on age group and rural/urban residence. Addressing these risk factors could improve concussion care access.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".