Bibliographic record
Abstract
Accurate diagnosis of mTBI is important in guiding appropriate acute management and follow-up care along with determining incidence rates. The research objectives were: (1) To investigate how mTBI is diagnosed in the primary care setting; (2) To determine the mTBI incidence rate in Ontario; (3) To investigate the use of CT scans in patients with suspected mTBI. This study involved a respective review of three-months of patient charts in 2002 from 12 EDs and 19 FP clinics. The study identified 876 potential mTBI patients. 360 of 876 cases had discrepant diagnoses from the primary care physicians and the secondary reviewer (kappa=0.19). The calculated incidence rate of mTBI from these data was 493 to 653/100,000. Significant predictors of CT scan prescription were seen in urban ED (OR=5.14;p0.001), documented LOC /or PTA (OR=4.83;p0.001), vomiting (OR=2.56;p0.01), arrival by ambulance (OR=2.15;p0.001), nausea (OR=1.92;p=0.02) and older age (OR=1.02;p0.01).
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".